Overview

Brought to you by YData

Dataset statistics

Number of variables81
Number of observations1472
Missing cells7904
Missing cells (%)6.6%
Total size in memory931.6 KiB
Average record size in memory648.1 B

Variable types

Numeric38
Text43

Alerts

LotFrontage has 259 (17.6%) missing values Missing
Alley has 1380 (93.8%) missing values Missing
MasVnrType has 881 (59.9%) missing values Missing
BsmtQual has 37 (2.5%) missing values Missing
BsmtCond has 37 (2.5%) missing values Missing
BsmtExposure has 38 (2.6%) missing values Missing
BsmtFinType1 has 37 (2.5%) missing values Missing
BsmtFinType2 has 38 (2.6%) missing values Missing
FireplaceQu has 698 (47.4%) missing values Missing
GarageType has 84 (5.7%) missing values Missing
GarageYrBlt has 84 (5.7%) missing values Missing
GarageFinish has 84 (5.7%) missing values Missing
GarageQual has 84 (5.7%) missing values Missing
GarageCond has 84 (5.7%) missing values Missing
PoolQC has 1465 (99.5%) missing values Missing
Fence has 1188 (80.7%) missing values Missing
MiscFeature has 1417 (96.3%) missing values Missing
MiscVal is highly skewed (γ1 = 24.04195078) Skewed
MasVnrArea has 870 (59.1%) zeros Zeros
BsmtFinSF1 has 472 (32.1%) zeros Zeros
BsmtFinSF2 has 1302 (88.5%) zeros Zeros
BsmtUnfSF has 120 (8.2%) zeros Zeros
TotalBsmtSF has 37 (2.5%) zeros Zeros
2ndFlrSF has 837 (56.9%) zeros Zeros
LowQualFinSF has 1446 (98.2%) zeros Zeros
BsmtFullBath has 862 (58.6%) zeros Zeros
BsmtHalfBath has 1390 (94.4%) zeros Zeros
HalfBath has 921 (62.6%) zeros Zeros
Fireplaces has 698 (47.4%) zeros Zeros
GarageCars has 84 (5.7%) zeros Zeros
GarageArea has 84 (5.7%) zeros Zeros
WoodDeckSF has 767 (52.1%) zeros Zeros
OpenPorchSF has 659 (44.8%) zeros Zeros
EnclosedPorch has 1263 (85.8%) zeros Zeros
3SsnPorch has 1448 (98.4%) zeros Zeros
ScreenPorch has 1356 (92.1%) zeros Zeros
PoolArea has 1465 (99.5%) zeros Zeros
MiscVal has 1419 (96.4%) zeros Zeros

Reproduction

Analysis started2025-02-07 23:28:52.654844
Analysis finished2025-02-07 23:28:53.968984
Duration1.31 second
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Id
Real number (ℝ)

Distinct1460
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean736.4021739
Minimum1
Maximum1460
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-02-08T04:58:54.094809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile74.55
Q1368.75
median736.5
Q31104.25
95-th percentile1398.45
Maximum1460
Range1459
Interquartile range (IQR)735.5

Descriptive statistics

Standard deviation424.9073334
Coefficient of variation (CV)0.5770044528
Kurtosis-1.201742256
Mean736.4021739
Median Absolute Deviation (MAD)368
Skewness-0.001317816177
Sum1083984
Variance180546.242
MonotonicityNot monotonic
2025-02-08T04:58:54.312048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1460 2
 
0.1%
1454 2
 
0.1%
1449 2
 
0.1%
1451 2
 
0.1%
1452 2
 
0.1%
1453 2
 
0.1%
1450 2
 
0.1%
1455 2
 
0.1%
1456 2
 
0.1%
1457 2
 
0.1%
Other values (1450) 1452
98.6%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
ValueCountFrequency (%)
1460 2
0.1%
1459 2
0.1%
1458 2
0.1%
1457 2
0.1%
1456 2
0.1%

MSSubClass
Real number (ℝ)

Distinct15
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.94293478
Minimum20
Maximum190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-02-08T04:58:54.431044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile20
Q120
median50
Q370
95-th percentile160
Maximum190
Range170
Interquartile range (IQR)50

Descriptive statistics

Standard deviation42.44757799
Coefficient of variation (CV)0.7454406443
Kurtosis1.573062665
Mean56.94293478
Median Absolute Deviation (MAD)30
Skewness1.40933619
Sum83820
Variance1801.796877
MonotonicityNot monotonic
2025-02-08T04:58:54.606013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
20 542
36.8%
60 300
20.4%
50 145
 
9.9%
120 87
 
5.9%
30 69
 
4.7%
160 63
 
4.3%
70 61
 
4.1%
80 58
 
3.9%
90 53
 
3.6%
190 30
 
2.0%
Other values (5) 64
 
4.3%
ValueCountFrequency (%)
20 542
36.8%
30 69
 
4.7%
40 4
 
0.3%
45 12
 
0.8%
50 145
 
9.9%
ValueCountFrequency (%)
190 30
 
2.0%
180 12
 
0.8%
160 63
4.3%
120 87
5.9%
90 53
3.6%

MSZoning
Text

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
2025-02-08T04:58:54.741186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length2
Mean length2.033967391
Min length2

Characters and Unicode

Total characters2994
Distinct characters12
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRL
2nd rowRL
3rd rowRL
4th rowRL
5th rowRL
ValueCountFrequency (%)
rl 1160
78.3%
rm 220
 
14.8%
fv 66
 
4.5%
rh 16
 
1.1%
c 10
 
0.7%
all 10
 
0.7%
2025-02-08T04:58:54.981766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 1396
46.6%
L 1160
38.7%
M 220
 
7.3%
F 66
 
2.2%
V 66
 
2.2%
l 20
 
0.7%
H 16
 
0.5%
C 10
 
0.3%
10
 
0.3%
( 10
 
0.3%
Other values (2) 20
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2934
98.0%
Lowercase Letter 30
 
1.0%
Space Separator 10
 
0.3%
Open Punctuation 10
 
0.3%
Close Punctuation 10
 
0.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 1396
47.6%
L 1160
39.5%
M 220
 
7.5%
F 66
 
2.2%
V 66
 
2.2%
H 16
 
0.5%
C 10
 
0.3%
Lowercase Letter
ValueCountFrequency (%)
l 20
66.7%
a 10
33.3%
Space Separator
ValueCountFrequency (%)
10
100.0%
Open Punctuation
ValueCountFrequency (%)
( 10
100.0%
Close Punctuation
ValueCountFrequency (%)
) 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2964
99.0%
Common 30
 
1.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 1396
47.1%
L 1160
39.1%
M 220
 
7.4%
F 66
 
2.2%
V 66
 
2.2%
l 20
 
0.7%
H 16
 
0.5%
C 10
 
0.3%
a 10
 
0.3%
Common
ValueCountFrequency (%)
10
33.3%
( 10
33.3%
) 10
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2994
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 1396
46.6%
L 1160
38.7%
M 220
 
7.3%
F 66
 
2.2%
V 66
 
2.2%
l 20
 
0.7%
H 16
 
0.5%
C 10
 
0.3%
10
 
0.3%
( 10
 
0.3%
Other values (2) 20
 
0.7%

LotFrontage
Real number (ℝ)

Missing 

Distinct110
Distinct (%)9.1%
Missing259
Missing (%)17.6%
Infinite0
Infinite (%)0.0%
Mean69.99340478
Minimum21
Maximum313
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-02-08T04:58:55.141007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile34
Q159
median69
Q380
95-th percentile107
Maximum313
Range292
Interquartile range (IQR)21

Descriptive statistics

Standard deviation24.24248288
Coefficient of variation (CV)0.3463538166
Kurtosis17.42919531
Mean69.99340478
Median Absolute Deviation (MAD)11
Skewness2.151570524
Sum84902
Variance587.6979763
MonotonicityNot monotonic
2025-02-08T04:58:55.301725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 144
 
9.8%
70 71
 
4.8%
80 69
 
4.7%
50 57
 
3.9%
75 54
 
3.7%
65 44
 
3.0%
85 41
 
2.8%
78 26
 
1.8%
21 24
 
1.6%
90 24
 
1.6%
Other values (100) 659
44.8%
(Missing) 259
 
17.6%
ValueCountFrequency (%)
21 24
1.6%
24 19
1.3%
30 6
 
0.4%
32 5
 
0.3%
33 1
 
0.1%
ValueCountFrequency (%)
313 2
0.1%
182 1
0.1%
174 2
0.1%
168 1
0.1%
160 1
0.1%

LotArea
Real number (ℝ)

Distinct1073
Distinct (%)72.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10505.64606
Minimum1300
Maximum215245
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-02-08T04:58:55.469343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1300
5-th percentile3277.3
Q17553.5
median9475
Q311601.5
95-th percentile17299.35
Maximum215245
Range213945
Interquartile range (IQR)4048

Descriptive statistics

Standard deviation9947.433778
Coefficient of variation (CV)0.9468654971
Kurtosis204.4069087
Mean10505.64606
Median Absolute Deviation (MAD)2000
Skewness12.23476226
Sum15464311
Variance98951438.77
MonotonicityNot monotonic
2025-02-08T04:58:55.641313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7200 25
 
1.7%
9600 24
 
1.6%
6000 17
 
1.2%
9000 15
 
1.0%
10800 14
 
1.0%
8400 14
 
1.0%
1680 10
 
0.7%
7500 10
 
0.7%
6240 8
 
0.5%
9100 8
 
0.5%
Other values (1063) 1327
90.1%
ValueCountFrequency (%)
1300 1
 
0.1%
1477 1
 
0.1%
1491 1
 
0.1%
1526 1
 
0.1%
1533 3
0.2%
ValueCountFrequency (%)
215245 1
0.1%
164660 1
0.1%
159000 1
0.1%
115149 1
0.1%
70761 1
0.1%

Street
Text

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
2025-02-08T04:58:55.752096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters5888
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPave
2nd rowPave
3rd rowPave
4th rowPave
5th rowPave
ValueCountFrequency (%)
pave 1466
99.6%
grvl 6
 
0.4%
2025-02-08T04:58:56.108330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
v 1472
25.0%
P 1466
24.9%
a 1466
24.9%
e 1466
24.9%
G 6
 
0.1%
r 6
 
0.1%
l 6
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4416
75.0%
Uppercase Letter 1472
 
25.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
v 1472
33.3%
a 1466
33.2%
e 1466
33.2%
r 6
 
0.1%
l 6
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
P 1466
99.6%
G 6
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 5888
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
v 1472
25.0%
P 1466
24.9%
a 1466
24.9%
e 1466
24.9%
G 6
 
0.1%
r 6
 
0.1%
l 6
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5888
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
v 1472
25.0%
P 1466
24.9%
a 1466
24.9%
e 1466
24.9%
G 6
 
0.1%
r 6
 
0.1%
l 6
 
0.1%

Alley
Text

Missing 

Distinct2
Distinct (%)2.2%
Missing1380
Missing (%)93.8%
Memory size11.6 KiB
2025-02-08T04:58:56.234265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters368
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGrvl
2nd rowPave
3rd rowPave
4th rowGrvl
5th rowPave
ValueCountFrequency (%)
grvl 50
54.3%
pave 42
45.7%
2025-02-08T04:58:56.626184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
v 92
25.0%
G 50
13.6%
r 50
13.6%
l 50
13.6%
P 42
11.4%
a 42
11.4%
e 42
11.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 276
75.0%
Uppercase Letter 92
 
25.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
v 92
33.3%
r 50
18.1%
l 50
18.1%
a 42
15.2%
e 42
15.2%
Uppercase Letter
ValueCountFrequency (%)
G 50
54.3%
P 42
45.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 368
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
v 92
25.0%
G 50
13.6%
r 50
13.6%
l 50
13.6%
P 42
11.4%
a 42
11.4%
e 42
11.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 368
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
v 92
25.0%
G 50
13.6%
r 50
13.6%
l 50
13.6%
P 42
11.4%
a 42
11.4%
e 42
11.4%

LotShape
Text

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
2025-02-08T04:58:56.753348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4416
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowReg
2nd rowReg
3rd rowIR1
4th rowIR1
5th rowIR1
ValueCountFrequency (%)
reg 937
63.7%
ir1 484
32.9%
ir2 41
 
2.8%
ir3 10
 
0.7%
2025-02-08T04:58:57.075181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 1472
33.3%
e 937
21.2%
g 937
21.2%
I 535
 
12.1%
1 484
 
11.0%
2 41
 
0.9%
3 10
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2007
45.4%
Lowercase Letter 1874
42.4%
Decimal Number 535
 
12.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 484
90.5%
2 41
 
7.7%
3 10
 
1.9%
Uppercase Letter
ValueCountFrequency (%)
R 1472
73.3%
I 535
 
26.7%
Lowercase Letter
ValueCountFrequency (%)
e 937
50.0%
g 937
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3881
87.9%
Common 535
 
12.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 1472
37.9%
e 937
24.1%
g 937
24.1%
I 535
 
13.8%
Common
ValueCountFrequency (%)
1 484
90.5%
2 41
 
7.7%
3 10
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 1472
33.3%
e 937
21.2%
g 937
21.2%
I 535
 
12.1%
1 484
 
11.0%
2 41
 
0.9%
3 10
 
0.2%

LandContour
Text

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
2025-02-08T04:58:57.246018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4416
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLvl
2nd rowLvl
3rd rowLvl
4th rowLvl
5th rowLvl
ValueCountFrequency (%)
lvl 1323
89.9%
bnk 63
 
4.3%
hls 50
 
3.4%
low 36
 
2.4%
2025-02-08T04:58:57.590935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
L 1409
31.9%
v 1323
30.0%
l 1323
30.0%
B 63
 
1.4%
n 63
 
1.4%
k 63
 
1.4%
H 50
 
1.1%
S 50
 
1.1%
o 36
 
0.8%
w 36
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2844
64.4%
Uppercase Letter 1572
35.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
v 1323
46.5%
l 1323
46.5%
n 63
 
2.2%
k 63
 
2.2%
o 36
 
1.3%
w 36
 
1.3%
Uppercase Letter
ValueCountFrequency (%)
L 1409
89.6%
B 63
 
4.0%
H 50
 
3.2%
S 50
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 4416
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
L 1409
31.9%
v 1323
30.0%
l 1323
30.0%
B 63
 
1.4%
n 63
 
1.4%
k 63
 
1.4%
H 50
 
1.1%
S 50
 
1.1%
o 36
 
0.8%
w 36
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
L 1409
31.9%
v 1323
30.0%
l 1323
30.0%
B 63
 
1.4%
n 63
 
1.4%
k 63
 
1.4%
H 50
 
1.1%
S 50
 
1.1%
o 36
 
0.8%
w 36
 
0.8%

Utilities
Text

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
2025-02-08T04:58:57.717956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters8832
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowAllPub
2nd rowAllPub
3rd rowAllPub
4th rowAllPub
5th rowAllPub
ValueCountFrequency (%)
allpub 1471
99.9%
nosewa 1
 
0.1%
2025-02-08T04:58:57.972494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
l 2942
33.3%
A 1471
16.7%
P 1471
16.7%
u 1471
16.7%
b 1471
16.7%
N 1
 
< 0.1%
o 1
 
< 0.1%
S 1
 
< 0.1%
e 1
 
< 0.1%
W 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5887
66.7%
Uppercase Letter 2945
33.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 2942
50.0%
u 1471
25.0%
b 1471
25.0%
o 1
 
< 0.1%
e 1
 
< 0.1%
a 1
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
A 1471
49.9%
P 1471
49.9%
N 1
 
< 0.1%
S 1
 
< 0.1%
W 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 8832
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 2942
33.3%
A 1471
16.7%
P 1471
16.7%
u 1471
16.7%
b 1471
16.7%
N 1
 
< 0.1%
o 1
 
< 0.1%
S 1
 
< 0.1%
e 1
 
< 0.1%
W 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8832
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 2942
33.3%
A 1471
16.7%
P 1471
16.7%
u 1471
16.7%
b 1471
16.7%
N 1
 
< 0.1%
o 1
 
< 0.1%
S 1
 
< 0.1%
e 1
 
< 0.1%
W 1
 
< 0.1%

LotConfig
Text

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
2025-02-08T04:58:58.100033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length5.957880435
Min length3

Characters and Unicode

Total characters8770
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowInside
2nd rowFR2
3rd rowInside
4th rowCorner
5th rowFR2
ValueCountFrequency (%)
inside 1063
72.2%
corner 263
 
17.9%
culdsac 94
 
6.4%
fr2 48
 
3.3%
fr3 4
 
0.3%
2025-02-08T04:58:58.443367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1326
15.1%
n 1326
15.1%
I 1063
12.1%
s 1063
12.1%
i 1063
12.1%
d 1063
12.1%
r 526
 
6.0%
C 357
 
4.1%
o 263
 
3.0%
S 94
 
1.1%
Other values (9) 626
7.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7006
79.9%
Uppercase Letter 1712
 
19.5%
Decimal Number 52
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1326
18.9%
n 1326
18.9%
s 1063
15.2%
i 1063
15.2%
d 1063
15.2%
r 526
 
7.5%
o 263
 
3.8%
c 94
 
1.3%
a 94
 
1.3%
u 94
 
1.3%
Uppercase Letter
ValueCountFrequency (%)
I 1063
62.1%
C 357
 
20.9%
S 94
 
5.5%
D 94
 
5.5%
F 52
 
3.0%
R 52
 
3.0%
Decimal Number
ValueCountFrequency (%)
2 48
92.3%
3 4
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 8718
99.4%
Common 52
 
0.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1326
15.2%
n 1326
15.2%
I 1063
12.2%
s 1063
12.2%
i 1063
12.2%
d 1063
12.2%
r 526
 
6.0%
C 357
 
4.1%
o 263
 
3.0%
S 94
 
1.1%
Other values (7) 574
6.6%
Common
ValueCountFrequency (%)
2 48
92.3%
3 4
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8770
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1326
15.1%
n 1326
15.1%
I 1063
12.1%
s 1063
12.1%
i 1063
12.1%
d 1063
12.1%
r 526
 
6.0%
C 357
 
4.1%
o 263
 
3.0%
S 94
 
1.1%
Other values (9) 626
7.1%

LandSlope
Text

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
2025-02-08T04:58:58.543700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4416
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGtl
2nd rowGtl
3rd rowGtl
4th rowGtl
5th rowGtl
ValueCountFrequency (%)
gtl 1394
94.7%
mod 65
 
4.4%
sev 13
 
0.9%
2025-02-08T04:58:58.759317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
G 1394
31.6%
t 1394
31.6%
l 1394
31.6%
M 65
 
1.5%
o 65
 
1.5%
d 65
 
1.5%
S 13
 
0.3%
e 13
 
0.3%
v 13
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2944
66.7%
Uppercase Letter 1472
33.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 1394
47.4%
l 1394
47.4%
o 65
 
2.2%
d 65
 
2.2%
e 13
 
0.4%
v 13
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
G 1394
94.7%
M 65
 
4.4%
S 13
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 4416
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
G 1394
31.6%
t 1394
31.6%
l 1394
31.6%
M 65
 
1.5%
o 65
 
1.5%
d 65
 
1.5%
S 13
 
0.3%
e 13
 
0.3%
v 13
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
G 1394
31.6%
t 1394
31.6%
l 1394
31.6%
M 65
 
1.5%
o 65
 
1.5%
d 65
 
1.5%
S 13
 
0.3%
e 13
 
0.3%
v 13
 
0.3%

Neighborhood
Text

Distinct25
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
2025-02-08T04:58:58.974218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.495244565
Min length5

Characters and Unicode

Total characters9561
Distinct characters38
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCollgCr
2nd rowVeenker
3rd rowCollgCr
4th rowCrawfor
5th rowNoRidge
ValueCountFrequency (%)
names 227
15.4%
collgcr 150
 
10.2%
oldtown 113
 
7.7%
edwards 103
 
7.0%
somerst 88
 
6.0%
gilbert 80
 
5.4%
nridght 77
 
5.2%
sawyer 74
 
5.0%
nwames 74
 
5.0%
sawyerw 59
 
4.0%
Other values (15) 427
29.0%
2025-02-08T04:58:59.353518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 939
 
9.8%
e 913
 
9.5%
l 624
 
6.5%
d 513
 
5.4%
s 494
 
5.2%
o 487
 
5.1%
m 444
 
4.6%
N 428
 
4.5%
w 419
 
4.4%
C 408
 
4.3%
Other values (28) 3892
40.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6826
71.4%
Uppercase Letter 2735
28.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 939
13.8%
e 913
13.4%
l 624
9.1%
d 513
 
7.5%
s 494
 
7.2%
o 487
 
7.1%
m 444
 
6.5%
w 419
 
6.1%
i 353
 
5.2%
a 350
 
5.1%
Other values (10) 1290
18.9%
Uppercase Letter
ValueCountFrequency (%)
N 428
15.6%
C 408
14.9%
S 354
12.9%
A 301
11.0%
T 188
6.9%
W 158
 
5.8%
O 150
 
5.5%
B 118
 
4.3%
R 115
 
4.2%
E 103
 
3.8%
Other values (8) 412
15.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 9561
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 939
 
9.8%
e 913
 
9.5%
l 624
 
6.5%
d 513
 
5.4%
s 494
 
5.2%
o 487
 
5.1%
m 444
 
4.6%
N 428
 
4.5%
w 419
 
4.4%
C 408
 
4.3%
Other values (28) 3892
40.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9561
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 939
 
9.8%
e 913
 
9.5%
l 624
 
6.5%
d 513
 
5.4%
s 494
 
5.2%
o 487
 
5.1%
m 444
 
4.6%
N 428
 
4.5%
w 419
 
4.4%
C 408
 
4.3%
Other values (28) 3892
40.7%

Condition1
Text

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
2025-02-08T04:58:59.509487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.120244565
Min length4

Characters and Unicode

Total characters6065
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNorm
2nd rowFeedr
3rd rowNorm
4th rowNorm
5th rowNorm
ValueCountFrequency (%)
norm 1272
86.4%
feedr 81
 
5.5%
artery 48
 
3.3%
rran 26
 
1.8%
posn 19
 
1.3%
rrae 11
 
0.7%
posa 8
 
0.5%
rrnn 5
 
0.3%
rrne 2
 
0.1%
2025-02-08T04:58:59.801031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 1449
23.9%
o 1299
21.4%
N 1298
21.4%
m 1272
21.0%
e 223
 
3.7%
A 93
 
1.5%
R 88
 
1.5%
F 81
 
1.3%
d 81
 
1.3%
t 48
 
0.8%
Other values (4) 133
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4478
73.8%
Uppercase Letter 1587
 
26.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 1449
32.4%
o 1299
29.0%
m 1272
28.4%
e 223
 
5.0%
d 81
 
1.8%
t 48
 
1.1%
y 48
 
1.1%
n 31
 
0.7%
s 27
 
0.6%
Uppercase Letter
ValueCountFrequency (%)
N 1298
81.8%
A 93
 
5.9%
R 88
 
5.5%
F 81
 
5.1%
P 27
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 6065
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 1449
23.9%
o 1299
21.4%
N 1298
21.4%
m 1272
21.0%
e 223
 
3.7%
A 93
 
1.5%
R 88
 
1.5%
F 81
 
1.3%
d 81
 
1.3%
t 48
 
0.8%
Other values (4) 133
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6065
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 1449
23.9%
o 1299
21.4%
N 1298
21.4%
m 1272
21.0%
e 223
 
3.7%
A 93
 
1.5%
R 88
 
1.5%
F 81
 
1.3%
d 81
 
1.3%
t 48
 
0.8%
Other values (4) 133
 
2.2%

Condition2
Text

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
2025-02-08T04:58:59.926459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.006793478
Min length4

Characters and Unicode

Total characters5898
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.2%

Sample

1st rowNorm
2nd rowNorm
3rd rowNorm
4th rowNorm
5th rowNorm
ValueCountFrequency (%)
norm 1457
99.0%
feedr 6
 
0.4%
artery 2
 
0.1%
rrnn 2
 
0.1%
posn 2
 
0.1%
posa 1
 
0.1%
rran 1
 
0.1%
rrae 1
 
0.1%
2025-02-08T04:59:00.238081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 1467
24.9%
N 1461
24.8%
o 1460
24.8%
m 1457
24.7%
e 15
 
0.3%
R 8
 
0.1%
F 6
 
0.1%
d 6
 
0.1%
A 5
 
0.1%
n 3
 
0.1%
Other values (4) 10
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4415
74.9%
Uppercase Letter 1483
 
25.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 1467
33.2%
o 1460
33.1%
m 1457
33.0%
e 15
 
0.3%
d 6
 
0.1%
n 3
 
0.1%
s 3
 
0.1%
t 2
 
< 0.1%
y 2
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
N 1461
98.5%
R 8
 
0.5%
F 6
 
0.4%
A 5
 
0.3%
P 3
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 5898
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 1467
24.9%
N 1461
24.8%
o 1460
24.8%
m 1457
24.7%
e 15
 
0.3%
R 8
 
0.1%
F 6
 
0.1%
d 6
 
0.1%
A 5
 
0.1%
n 3
 
0.1%
Other values (4) 10
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5898
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 1467
24.9%
N 1461
24.8%
o 1460
24.8%
m 1457
24.7%
e 15
 
0.3%
R 8
 
0.1%
F 6
 
0.1%
d 6
 
0.1%
A 5
 
0.1%
n 3
 
0.1%
Other values (4) 10
 
0.2%

BldgType
Text

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
2025-02-08T04:59:00.388897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.300271739
Min length4

Characters and Unicode

Total characters6330
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1Fam
2nd row1Fam
3rd row1Fam
4th row1Fam
5th row1Fam
ValueCountFrequency (%)
1fam 1229
83.5%
twnhse 115
 
7.8%
duplex 53
 
3.6%
twnhs 44
 
3.0%
2fmcon 31
 
2.1%
2025-02-08T04:59:00.767421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
m 1260
19.9%
1 1229
19.4%
a 1229
19.4%
F 1229
19.4%
n 190
 
3.0%
T 159
 
2.5%
w 159
 
2.5%
h 159
 
2.5%
s 159
 
2.5%
E 115
 
1.8%
Other values (10) 442
 
7.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3483
55.0%
Uppercase Letter 1587
25.1%
Decimal Number 1260
 
19.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
m 1260
36.2%
a 1229
35.3%
n 190
 
5.5%
w 159
 
4.6%
h 159
 
4.6%
s 159
 
4.6%
l 53
 
1.5%
x 53
 
1.5%
e 53
 
1.5%
p 53
 
1.5%
Other values (3) 115
 
3.3%
Uppercase Letter
ValueCountFrequency (%)
F 1229
77.4%
T 159
 
10.0%
E 115
 
7.2%
D 53
 
3.3%
C 31
 
2.0%
Decimal Number
ValueCountFrequency (%)
1 1229
97.5%
2 31
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 5070
80.1%
Common 1260
 
19.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
m 1260
24.9%
a 1229
24.2%
F 1229
24.2%
n 190
 
3.7%
T 159
 
3.1%
w 159
 
3.1%
h 159
 
3.1%
s 159
 
3.1%
E 115
 
2.3%
l 53
 
1.0%
Other values (8) 358
 
7.1%
Common
ValueCountFrequency (%)
1 1229
97.5%
2 31
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6330
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
m 1260
19.9%
1 1229
19.4%
a 1229
19.4%
F 1229
19.4%
n 190
 
3.0%
T 159
 
2.5%
w 159
 
2.5%
h 159
 
2.5%
s 159
 
2.5%
E 115
 
1.8%
Other values (10) 442
 
7.0%

HouseStyle
Text

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
2025-02-08T04:59:00.932371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.910326087
Min length4

Characters and Unicode

Total characters8700
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2Story
2nd row1Story
3rd row2Story
4th row2Story
5th row2Story
ValueCountFrequency (%)
1story 732
49.7%
2story 449
30.5%
1.5fin 154
 
10.5%
slvl 66
 
4.5%
sfoyer 38
 
2.6%
1.5unf 14
 
1.0%
2.5unf 11
 
0.7%
2.5fin 8
 
0.5%
2025-02-08T04:59:01.298219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 1285
14.8%
o 1219
14.0%
r 1219
14.0%
y 1219
14.0%
t 1181
13.6%
1 900
10.3%
2 468
 
5.4%
F 200
 
2.3%
5 187
 
2.1%
. 187
 
2.1%
Other values (8) 635
7.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5382
61.9%
Uppercase Letter 1576
 
18.1%
Decimal Number 1555
 
17.9%
Other Punctuation 187
 
2.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 1219
22.6%
r 1219
22.6%
y 1219
22.6%
t 1181
21.9%
n 187
 
3.5%
i 162
 
3.0%
v 66
 
1.2%
l 66
 
1.2%
e 38
 
0.7%
f 25
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
S 1285
81.5%
F 200
 
12.7%
L 66
 
4.2%
U 25
 
1.6%
Decimal Number
ValueCountFrequency (%)
1 900
57.9%
2 468
30.1%
5 187
 
12.0%
Other Punctuation
ValueCountFrequency (%)
. 187
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6958
80.0%
Common 1742
 
20.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 1285
18.5%
o 1219
17.5%
r 1219
17.5%
y 1219
17.5%
t 1181
17.0%
F 200
 
2.9%
n 187
 
2.7%
i 162
 
2.3%
L 66
 
0.9%
v 66
 
0.9%
Other values (4) 154
 
2.2%
Common
ValueCountFrequency (%)
1 900
51.7%
2 468
26.9%
5 187
 
10.7%
. 187
 
10.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 1285
14.8%
o 1219
14.0%
r 1219
14.0%
y 1219
14.0%
t 1181
13.6%
1 900
10.3%
2 468
 
5.4%
F 200
 
2.3%
5 187
 
2.1%
. 187
 
2.1%
Other values (8) 635
7.3%

OverallQual
Real number (ℝ)

Distinct10
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.095788043
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-02-08T04:59:01.458586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median6
Q37
95-th percentile8
Maximum10
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.381506947
Coefficient of variation (CV)0.2266330354
Kurtosis0.09442388155
Mean6.095788043
Median Absolute Deviation (MAD)1
Skewness0.2212409784
Sum8973
Variance1.908561445
MonotonicityNot monotonic
2025-02-08T04:59:01.626640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
5 403
27.4%
6 376
25.5%
7 321
21.8%
8 169
11.5%
4 117
 
7.9%
9 43
 
2.9%
3 20
 
1.4%
10 18
 
1.2%
2 3
 
0.2%
1 2
 
0.1%
ValueCountFrequency (%)
1 2
 
0.1%
2 3
 
0.2%
3 20
 
1.4%
4 117
 
7.9%
5 403
27.4%
ValueCountFrequency (%)
10 18
 
1.2%
9 43
 
2.9%
8 169
11.5%
7 321
21.8%
6 376
25.5%

OverallCond
Real number (ℝ)

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.578125
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-02-08T04:59:01.809761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median5
Q36
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.113850636
Coefficient of variation (CV)0.1996819067
Kurtosis1.115334337
Mean5.578125
Median Absolute Deviation (MAD)0
Skewness0.7001414031
Sum8211
Variance1.240663239
MonotonicityNot monotonic
2025-02-08T04:59:01.952981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
5 827
56.2%
6 255
 
17.3%
7 207
 
14.1%
8 72
 
4.9%
4 57
 
3.9%
3 25
 
1.7%
9 23
 
1.6%
2 5
 
0.3%
1 1
 
0.1%
ValueCountFrequency (%)
1 1
 
0.1%
2 5
 
0.3%
3 25
 
1.7%
4 57
 
3.9%
5 827
56.2%
ValueCountFrequency (%)
9 23
 
1.6%
8 72
 
4.9%
7 207
 
14.1%
6 255
 
17.3%
5 827
56.2%

YearBuilt
Real number (ℝ)

Distinct112
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1971.305027
Minimum1872
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-02-08T04:59:02.107169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1872
5-th percentile1916
Q11954
median1973
Q32000.25
95-th percentile2007
Maximum2010
Range138
Interquartile range (IQR)46.25

Descriptive statistics

Standard deviation30.20066633
Coefficient of variation (CV)0.01532013864
Kurtosis-0.4391442332
Mean1971.305027
Median Absolute Deviation (MAD)25
Skewness-0.6142895608
Sum2901761
Variance912.0802466
MonotonicityNot monotonic
2025-02-08T04:59:02.257697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2006 68
 
4.6%
2005 65
 
4.4%
2004 55
 
3.7%
2007 49
 
3.3%
2003 45
 
3.1%
1976 33
 
2.2%
1977 32
 
2.2%
1920 30
 
2.0%
1999 26
 
1.8%
1959 26
 
1.8%
Other values (102) 1043
70.9%
ValueCountFrequency (%)
1872 1
 
0.1%
1875 1
 
0.1%
1880 4
0.3%
1882 1
 
0.1%
1885 2
0.1%
ValueCountFrequency (%)
2010 1
 
0.1%
2009 18
 
1.2%
2008 24
 
1.6%
2007 49
3.3%
2006 68
4.6%

YearRemodAdd
Real number (ℝ)

Distinct61
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1984.9375
Minimum1950
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-02-08T04:59:02.431770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1950
5-th percentile1950
Q11967
median1994
Q32004
95-th percentile2007
Maximum2010
Range60
Interquartile range (IQR)37

Descriptive statistics

Standard deviation20.62052745
Coefficient of variation (CV)0.01038850213
Kurtosis-1.265755797
Mean1984.9375
Median Absolute Deviation (MAD)13
Skewness-0.508201817
Sum2921828
Variance425.2061523
MonotonicityNot monotonic
2025-02-08T04:59:02.668605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1950 178
 
12.1%
2006 99
 
6.7%
2007 76
 
5.2%
2005 75
 
5.1%
2004 62
 
4.2%
2000 57
 
3.9%
2003 51
 
3.5%
2002 48
 
3.3%
2008 40
 
2.7%
1996 37
 
2.5%
Other values (51) 749
50.9%
ValueCountFrequency (%)
1950 178
12.1%
1951 4
 
0.3%
1952 5
 
0.3%
1953 10
 
0.7%
1954 14
 
1.0%
ValueCountFrequency (%)
2010 6
 
0.4%
2009 24
 
1.6%
2008 40
2.7%
2007 76
5.2%
2006 99
6.7%

RoofStyle
Text

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
2025-02-08T04:59:02.836396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length4.624320652
Min length3

Characters and Unicode

Total characters6807
Distinct characters18
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGable
2nd rowGable
3rd rowGable
4th rowGable
5th rowGable
ValueCountFrequency (%)
gable 1152
78.3%
hip 287
 
19.5%
flat 13
 
0.9%
gambrel 11
 
0.7%
mansard 7
 
0.5%
shed 2
 
0.1%
2025-02-08T04:59:03.381810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1190
17.5%
l 1176
17.3%
e 1165
17.1%
G 1163
17.1%
b 1163
17.1%
H 287
 
4.2%
i 287
 
4.2%
p 287
 
4.2%
r 18
 
0.3%
t 13
 
0.2%
Other values (8) 58
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5335
78.4%
Uppercase Letter 1472
 
21.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1190
22.3%
l 1176
22.0%
e 1165
21.8%
b 1163
21.8%
i 287
 
5.4%
p 287
 
5.4%
r 18
 
0.3%
t 13
 
0.2%
m 11
 
0.2%
d 9
 
0.2%
Other values (3) 16
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
G 1163
79.0%
H 287
 
19.5%
F 13
 
0.9%
M 7
 
0.5%
S 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 6807
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1190
17.5%
l 1176
17.3%
e 1165
17.1%
G 1163
17.1%
b 1163
17.1%
H 287
 
4.2%
i 287
 
4.2%
p 287
 
4.2%
r 18
 
0.3%
t 13
 
0.2%
Other values (8) 58
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6807
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1190
17.5%
l 1176
17.3%
e 1165
17.1%
G 1163
17.1%
b 1163
17.1%
H 287
 
4.2%
i 287
 
4.2%
p 287
 
4.2%
r 18
 
0.3%
t 13
 
0.2%
Other values (8) 58
 
0.9%

RoofMatl
Text

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
2025-02-08T04:59:03.601950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.996603261
Min length4

Characters and Unicode

Total characters10299
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.3%

Sample

1st rowCompShg
2nd rowCompShg
3rd rowCompShg
4th rowCompShg
5th rowCompShg
ValueCountFrequency (%)
compshg 1446
98.2%
tar&grv 11
 
0.7%
wdshngl 6
 
0.4%
wdshake 5
 
0.3%
metal 1
 
0.1%
membran 1
 
0.1%
roll 1
 
0.1%
clytile 1
 
0.1%
2025-02-08T04:59:04.022235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 1457
14.1%
h 1457
14.1%
g 1452
14.1%
C 1447
14.0%
m 1447
14.0%
o 1447
14.0%
p 1446
14.0%
r 23
 
0.2%
a 18
 
0.2%
T 12
 
0.1%
Other values (15) 93
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7347
71.3%
Uppercase Letter 2941
28.6%
Other Punctuation 11
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
h 1457
19.8%
g 1452
19.8%
m 1447
19.7%
o 1447
19.7%
p 1446
19.7%
r 23
 
0.3%
a 18
 
0.2%
l 11
 
0.1%
d 11
 
0.1%
v 11
 
0.1%
Other values (7) 24
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
S 1457
49.5%
C 1447
49.2%
T 12
 
0.4%
W 11
 
0.4%
G 11
 
0.4%
M 2
 
0.1%
R 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
& 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10288
99.9%
Common 11
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 1457
14.2%
h 1457
14.2%
g 1452
14.1%
C 1447
14.1%
m 1447
14.1%
o 1447
14.1%
p 1446
14.1%
r 23
 
0.2%
a 18
 
0.2%
T 12
 
0.1%
Other values (14) 82
 
0.8%
Common
ValueCountFrequency (%)
& 11
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10299
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 1457
14.1%
h 1457
14.1%
g 1452
14.1%
C 1447
14.0%
m 1447
14.0%
o 1447
14.0%
p 1446
14.0%
r 23
 
0.2%
a 18
 
0.2%
T 12
 
0.1%
Other values (15) 93
 
0.9%

Exterior1st
Text

Distinct15
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
2025-02-08T04:59:04.179969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.979619565
Min length5

Characters and Unicode

Total characters10274
Distinct characters32
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.2%

Sample

1st rowVinylSd
2nd rowMetalSd
3rd rowVinylSd
4th rowWd Sdng
5th rowVinylSd
ValueCountFrequency (%)
vinylsd 520
31.0%
hdboard 223
13.3%
metalsd 222
13.2%
wd 206
 
12.3%
sdng 206
 
12.3%
plywood 109
 
6.5%
cemntbd 64
 
3.8%
brkface 50
 
3.0%
wdshing 26
 
1.5%
stucco 25
 
1.5%
Other values (6) 27
 
1.6%
2025-02-08T04:59:04.564418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
d 1799
17.5%
S 1023
 
10.0%
l 852
 
8.3%
n 839
 
8.2%
y 629
 
6.1%
i 546
 
5.3%
V 520
 
5.1%
a 495
 
4.8%
o 471
 
4.6%
B 340
 
3.3%
Other values (22) 2760
26.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7260
70.7%
Uppercase Letter 2808
 
27.3%
Space Separator 206
 
2.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 1799
24.8%
l 852
11.7%
n 839
11.6%
y 629
 
8.7%
i 546
 
7.5%
a 495
 
6.8%
o 471
 
6.5%
e 338
 
4.7%
t 314
 
4.3%
r 275
 
3.8%
Other values (10) 702
 
9.7%
Uppercase Letter
ValueCountFrequency (%)
S 1023
36.4%
V 520
18.5%
B 340
 
12.1%
W 232
 
8.3%
H 223
 
7.9%
M 222
 
7.9%
P 109
 
3.9%
C 67
 
2.4%
F 50
 
1.8%
A 21
 
0.7%
Space Separator
ValueCountFrequency (%)
206
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10068
98.0%
Common 206
 
2.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 1799
17.9%
S 1023
10.2%
l 852
 
8.5%
n 839
 
8.3%
y 629
 
6.2%
i 546
 
5.4%
V 520
 
5.2%
a 495
 
4.9%
o 471
 
4.7%
B 340
 
3.4%
Other values (21) 2554
25.4%
Common
ValueCountFrequency (%)
206
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10274
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 1799
17.5%
S 1023
 
10.0%
l 852
 
8.3%
n 839
 
8.2%
y 629
 
6.1%
i 546
 
5.3%
V 520
 
5.1%
a 495
 
4.8%
o 471
 
4.6%
B 340
 
3.3%
Other values (22) 2760
26.9%

Exterior2nd
Text

Distinct16
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
2025-02-08T04:59:04.713567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.973505435
Min length5

Characters and Unicode

Total characters10265
Distinct characters33
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowVinylSd
2nd rowMetalSd
3rd rowVinylSd
4th rowWd Shng
5th rowVinylSd
ValueCountFrequency (%)
vinylsd 509
29.7%
wd 235
13.7%
metalsd 215
12.5%
hdboard 209
12.2%
sdng 197
 
11.5%
plywood 143
 
8.3%
cmentbd 63
 
3.7%
shng 38
 
2.2%
stucco 26
 
1.5%
brkface 25
 
1.5%
Other values (8) 54
 
3.2%
2025-02-08T04:59:05.189259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
d 1780
17.3%
S 1023
 
10.0%
l 868
 
8.5%
n 842
 
8.2%
y 652
 
6.4%
o 527
 
5.1%
V 509
 
5.0%
i 509
 
5.0%
a 449
 
4.4%
t 320
 
3.1%
Other values (23) 2786
27.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7254
70.7%
Uppercase Letter 2769
 
27.0%
Space Separator 242
 
2.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 1780
24.5%
l 868
12.0%
n 842
11.6%
y 652
 
9.0%
o 527
 
7.3%
i 509
 
7.0%
a 449
 
6.2%
t 320
 
4.4%
e 309
 
4.3%
g 255
 
3.5%
Other values (10) 743
10.2%
Uppercase Letter
ValueCountFrequency (%)
S 1023
36.9%
V 509
18.4%
B 305
 
11.0%
W 235
 
8.5%
M 215
 
7.8%
H 209
 
7.5%
P 143
 
5.2%
C 71
 
2.6%
F 25
 
0.9%
A 23
 
0.8%
Other values (2) 11
 
0.4%
Space Separator
ValueCountFrequency (%)
242
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10023
97.6%
Common 242
 
2.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 1780
17.8%
S 1023
10.2%
l 868
 
8.7%
n 842
 
8.4%
y 652
 
6.5%
o 527
 
5.3%
V 509
 
5.1%
i 509
 
5.1%
a 449
 
4.5%
t 320
 
3.2%
Other values (22) 2544
25.4%
Common
ValueCountFrequency (%)
242
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10265
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 1780
17.3%
S 1023
 
10.0%
l 868
 
8.5%
n 842
 
8.2%
y 652
 
6.4%
o 527
 
5.1%
V 509
 
5.0%
i 509
 
5.0%
a 449
 
4.4%
t 320
 
3.1%
Other values (23) 2786
27.1%

MasVnrType
Text

Missing 

Distinct3
Distinct (%)0.5%
Missing881
Missing (%)59.9%
Memory size11.6 KiB
2025-02-08T04:59:05.330807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.534686971
Min length5

Characters and Unicode

Total characters3862
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBrkFace
2nd rowBrkFace
3rd rowBrkFace
4th rowStone
5th rowStone
ValueCountFrequency (%)
brkface 446
75.5%
stone 130
 
22.0%
brkcmn 15
 
2.5%
2025-02-08T04:59:05.620068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 576
14.9%
B 461
11.9%
r 461
11.9%
k 461
11.9%
F 446
11.5%
a 446
11.5%
c 446
11.5%
n 145
 
3.8%
S 130
 
3.4%
t 130
 
3.4%
Other values (3) 160
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2810
72.8%
Uppercase Letter 1052
 
27.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 576
20.5%
r 461
16.4%
k 461
16.4%
a 446
15.9%
c 446
15.9%
n 145
 
5.2%
t 130
 
4.6%
o 130
 
4.6%
m 15
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
B 461
43.8%
F 446
42.4%
S 130
 
12.4%
C 15
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 3862
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 576
14.9%
B 461
11.9%
r 461
11.9%
k 461
11.9%
F 446
11.5%
a 446
11.5%
c 446
11.5%
n 145
 
3.8%
S 130
 
3.4%
t 130
 
3.4%
Other values (3) 160
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3862
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 576
14.9%
B 461
11.9%
r 461
11.9%
k 461
11.9%
F 446
11.5%
a 446
11.5%
c 446
11.5%
n 145
 
3.8%
S 130
 
3.4%
t 130
 
3.4%
Other values (3) 160
 
4.1%

MasVnrArea
Real number (ℝ)

Zeros 

Distinct327
Distinct (%)22.3%
Missing8
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean103.1038251
Minimum0
Maximum1600
Zeros870
Zeros (%)59.1%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-02-08T04:59:05.820141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3164.25
95-th percentile455.4
Maximum1600
Range1600
Interquartile range (IQR)164.25

Descriptive statistics

Standard deviation180.5213953
Coefficient of variation (CV)1.75087001
Kurtosis10.16735829
Mean103.1038251
Median Absolute Deviation (MAD)0
Skewness2.679794517
Sum150944
Variance32587.97418
MonotonicityNot monotonic
2025-02-08T04:59:05.994813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 870
59.1%
72 8
 
0.5%
108 8
 
0.5%
180 8
 
0.5%
120 7
 
0.5%
16 7
 
0.5%
80 7
 
0.5%
106 6
 
0.4%
200 6
 
0.4%
340 6
 
0.4%
Other values (317) 531
36.1%
(Missing) 8
 
0.5%
ValueCountFrequency (%)
0 870
59.1%
1 2
 
0.1%
11 1
 
0.1%
14 1
 
0.1%
16 7
 
0.5%
ValueCountFrequency (%)
1600 1
0.1%
1378 1
0.1%
1170 1
0.1%
1129 1
0.1%
1115 1
0.1%

ExterQual
Text

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
2025-02-08T04:59:06.104556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2944
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGd
2nd rowTA
3rd rowGd
4th rowTA
5th rowGd
ValueCountFrequency (%)
ta 914
62.1%
gd 491
33.4%
ex 53
 
3.6%
fa 14
 
1.0%
2025-02-08T04:59:06.580482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T 914
31.0%
A 914
31.0%
G 491
16.7%
d 491
16.7%
E 53
 
1.8%
x 53
 
1.8%
F 14
 
0.5%
a 14
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2386
81.0%
Lowercase Letter 558
 
19.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 914
38.3%
A 914
38.3%
G 491
20.6%
E 53
 
2.2%
F 14
 
0.6%
Lowercase Letter
ValueCountFrequency (%)
d 491
88.0%
x 53
 
9.5%
a 14
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 2944
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 914
31.0%
A 914
31.0%
G 491
16.7%
d 491
16.7%
E 53
 
1.8%
x 53
 
1.8%
F 14
 
0.5%
a 14
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2944
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 914
31.0%
A 914
31.0%
G 491
16.7%
d 491
16.7%
E 53
 
1.8%
x 53
 
1.8%
F 14
 
0.5%
a 14
 
0.5%

ExterCond
Text

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
2025-02-08T04:59:06.749519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2944
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowTA
2nd rowTA
3rd rowTA
4th rowTA
5th rowTA
ValueCountFrequency (%)
ta 1293
87.8%
gd 147
 
10.0%
fa 28
 
1.9%
ex 3
 
0.2%
po 1
 
0.1%
2025-02-08T04:59:07.033859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T 1293
43.9%
A 1293
43.9%
G 147
 
5.0%
d 147
 
5.0%
F 28
 
1.0%
a 28
 
1.0%
E 3
 
0.1%
x 3
 
0.1%
P 1
 
< 0.1%
o 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2765
93.9%
Lowercase Letter 179
 
6.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 1293
46.8%
A 1293
46.8%
G 147
 
5.3%
F 28
 
1.0%
E 3
 
0.1%
P 1
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
d 147
82.1%
a 28
 
15.6%
x 3
 
1.7%
o 1
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 2944
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 1293
43.9%
A 1293
43.9%
G 147
 
5.0%
d 147
 
5.0%
F 28
 
1.0%
a 28
 
1.0%
E 3
 
0.1%
x 3
 
0.1%
P 1
 
< 0.1%
o 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2944
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 1293
43.9%
A 1293
43.9%
G 147
 
5.0%
d 147
 
5.0%
F 28
 
1.0%
a 28
 
1.0%
E 3
 
0.1%
x 3
 
0.1%
P 1
 
< 0.1%
o 1
 
< 0.1%

Foundation
Text

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
2025-02-08T04:59:07.186325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.515625
Min length4

Characters and Unicode

Total characters8119
Distinct characters18
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPConc
2nd rowCBlock
3rd rowPConc
4th rowBrkTil
5th rowPConc
ValueCountFrequency (%)
pconc 652
44.3%
cblock 640
43.5%
brktil 146
 
9.9%
slab 24
 
1.6%
stone 7
 
0.5%
wood 3
 
0.2%
2025-02-08T04:59:07.555295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 1305
16.1%
C 1292
15.9%
c 1292
15.9%
l 810
10.0%
B 786
9.7%
k 786
9.7%
n 659
8.1%
P 652
8.0%
i 146
 
1.8%
T 146
 
1.8%
Other values (8) 245
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5209
64.2%
Uppercase Letter 2910
35.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 1305
25.1%
c 1292
24.8%
l 810
15.6%
k 786
15.1%
n 659
12.7%
i 146
 
2.8%
r 146
 
2.8%
a 24
 
0.5%
b 24
 
0.5%
t 7
 
0.1%
Other values (2) 10
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
C 1292
44.4%
B 786
27.0%
P 652
22.4%
T 146
 
5.0%
S 31
 
1.1%
W 3
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 8119
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 1305
16.1%
C 1292
15.9%
c 1292
15.9%
l 810
10.0%
B 786
9.7%
k 786
9.7%
n 659
8.1%
P 652
8.0%
i 146
 
1.8%
T 146
 
1.8%
Other values (8) 245
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8119
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 1305
16.1%
C 1292
15.9%
c 1292
15.9%
l 810
10.0%
B 786
9.7%
k 786
9.7%
n 659
8.1%
P 652
8.0%
i 146
 
1.8%
T 146
 
1.8%
Other values (8) 245
 
3.0%

BsmtQual
Text

Missing 

Distinct4
Distinct (%)0.3%
Missing37
Missing (%)2.5%
Memory size11.6 KiB
2025-02-08T04:59:07.730348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2870
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGd
2nd rowGd
3rd rowGd
4th rowTA
5th rowGd
ValueCountFrequency (%)
ta 652
45.4%
gd 626
43.6%
ex 121
 
8.4%
fa 36
 
2.5%
2025-02-08T04:59:08.069468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T 652
22.7%
A 652
22.7%
G 626
21.8%
d 626
21.8%
E 121
 
4.2%
x 121
 
4.2%
F 36
 
1.3%
a 36
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2087
72.7%
Lowercase Letter 783
 
27.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 652
31.2%
A 652
31.2%
G 626
30.0%
E 121
 
5.8%
F 36
 
1.7%
Lowercase Letter
ValueCountFrequency (%)
d 626
79.9%
x 121
 
15.5%
a 36
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 2870
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 652
22.7%
A 652
22.7%
G 626
21.8%
d 626
21.8%
E 121
 
4.2%
x 121
 
4.2%
F 36
 
1.3%
a 36
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2870
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 652
22.7%
A 652
22.7%
G 626
21.8%
d 626
21.8%
E 121
 
4.2%
x 121
 
4.2%
F 36
 
1.3%
a 36
 
1.3%

BsmtCond
Text

Missing 

Distinct4
Distinct (%)0.3%
Missing37
Missing (%)2.5%
Memory size11.6 KiB
2025-02-08T04:59:08.210319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2870
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTA
2nd rowTA
3rd rowTA
4th rowGd
5th rowTA
ValueCountFrequency (%)
ta 1322
92.1%
gd 66
 
4.6%
fa 45
 
3.1%
po 2
 
0.1%
2025-02-08T04:59:08.539126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T 1322
46.1%
A 1322
46.1%
G 66
 
2.3%
d 66
 
2.3%
F 45
 
1.6%
a 45
 
1.6%
P 2
 
0.1%
o 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2757
96.1%
Lowercase Letter 113
 
3.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 1322
48.0%
A 1322
48.0%
G 66
 
2.4%
F 45
 
1.6%
P 2
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
d 66
58.4%
a 45
39.8%
o 2
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 2870
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 1322
46.1%
A 1322
46.1%
G 66
 
2.3%
d 66
 
2.3%
F 45
 
1.6%
a 45
 
1.6%
P 2
 
0.1%
o 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2870
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 1322
46.1%
A 1322
46.1%
G 66
 
2.3%
d 66
 
2.3%
F 45
 
1.6%
a 45
 
1.6%
P 2
 
0.1%
o 2
 
0.1%

BsmtExposure
Text

Missing 

Distinct4
Distinct (%)0.3%
Missing38
Missing (%)2.6%
Memory size11.6 KiB
2025-02-08T04:59:08.663776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2868
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowGd
3rd rowMn
4th rowNo
5th rowAv
ValueCountFrequency (%)
no 962
67.1%
av 222
 
15.5%
gd 135
 
9.4%
mn 115
 
8.0%
2025-02-08T04:59:08.919068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 962
33.5%
o 962
33.5%
A 222
 
7.7%
v 222
 
7.7%
G 135
 
4.7%
d 135
 
4.7%
M 115
 
4.0%
n 115
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1434
50.0%
Lowercase Letter 1434
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 962
67.1%
A 222
 
15.5%
G 135
 
9.4%
M 115
 
8.0%
Lowercase Letter
ValueCountFrequency (%)
o 962
67.1%
v 222
 
15.5%
d 135
 
9.4%
n 115
 
8.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2868
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 962
33.5%
o 962
33.5%
A 222
 
7.7%
v 222
 
7.7%
G 135
 
4.7%
d 135
 
4.7%
M 115
 
4.0%
n 115
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2868
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 962
33.5%
o 962
33.5%
A 222
 
7.7%
v 222
 
7.7%
G 135
 
4.7%
d 135
 
4.7%
M 115
 
4.0%
n 115
 
4.0%

BsmtFinType1
Text

Missing 

Distinct6
Distinct (%)0.4%
Missing37
Missing (%)2.5%
Memory size11.6 KiB
2025-02-08T04:59:09.055680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4305
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGLQ
2nd rowALQ
3rd rowGLQ
4th rowALQ
5th rowGLQ
ValueCountFrequency (%)
unf 435
30.3%
glq 423
29.5%
alq 221
15.4%
blq 149
 
10.4%
rec 133
 
9.3%
lwq 74
 
5.2%
2025-02-08T04:59:09.459385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
L 867
20.1%
Q 867
20.1%
U 435
10.1%
n 435
10.1%
f 435
10.1%
G 423
9.8%
A 221
 
5.1%
B 149
 
3.5%
R 133
 
3.1%
e 133
 
3.1%
Other values (2) 207
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3095
71.9%
Lowercase Letter 1210
 
28.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L 867
28.0%
Q 867
28.0%
U 435
14.1%
G 423
13.7%
A 221
 
7.1%
B 149
 
4.8%
R 133
 
4.3%
Lowercase Letter
ValueCountFrequency (%)
n 435
36.0%
f 435
36.0%
e 133
 
11.0%
c 133
 
11.0%
w 74
 
6.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 4305
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
L 867
20.1%
Q 867
20.1%
U 435
10.1%
n 435
10.1%
f 435
10.1%
G 423
9.8%
A 221
 
5.1%
B 149
 
3.5%
R 133
 
3.1%
e 133
 
3.1%
Other values (2) 207
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4305
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
L 867
20.1%
Q 867
20.1%
U 435
10.1%
n 435
10.1%
f 435
10.1%
G 423
9.8%
A 221
 
5.1%
B 149
 
3.5%
R 133
 
3.1%
e 133
 
3.1%
Other values (2) 207
 
4.8%

BsmtFinSF1
Real number (ℝ)

Zeros 

Distinct637
Distinct (%)43.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean442.3695652
Minimum0
Maximum5644
Zeros472
Zeros (%)32.1%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-02-08T04:59:09.704399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median382
Q3712
95-th percentile1271.8
Maximum5644
Range5644
Interquartile range (IQR)712

Descriptive statistics

Standard deviation455.3243135
Coefficient of variation (CV)1.029284899
Kurtosis11.12093741
Mean442.3695652
Median Absolute Deviation (MAD)382
Skewness1.685758529
Sum651168
Variance207320.2304
MonotonicityNot monotonic
2025-02-08T04:59:09.919740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 472
32.1%
24 12
 
0.8%
16 9
 
0.6%
616 5
 
0.3%
662 5
 
0.3%
547 5
 
0.3%
686 5
 
0.3%
553 5
 
0.3%
20 5
 
0.3%
936 5
 
0.3%
Other values (627) 944
64.1%
ValueCountFrequency (%)
0 472
32.1%
2 1
 
0.1%
16 9
 
0.6%
20 5
 
0.3%
24 12
 
0.8%
ValueCountFrequency (%)
5644 1
0.1%
2260 1
0.1%
2188 1
0.1%
2096 1
0.1%
1904 1
0.1%

BsmtFinType2
Text

Missing 

Distinct6
Distinct (%)0.4%
Missing38
Missing (%)2.6%
Memory size11.6 KiB
2025-02-08T04:59:10.070882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4302
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnf
2nd rowUnf
3rd rowUnf
4th rowUnf
5th rowUnf
ValueCountFrequency (%)
unf 1265
88.2%
rec 56
 
3.9%
lwq 47
 
3.3%
blq 33
 
2.3%
alq 19
 
1.3%
glq 14
 
1.0%
2025-02-08T04:59:10.385123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
U 1265
29.4%
n 1265
29.4%
f 1265
29.4%
L 113
 
2.6%
Q 113
 
2.6%
R 56
 
1.3%
e 56
 
1.3%
c 56
 
1.3%
w 47
 
1.1%
B 33
 
0.8%
Other values (2) 33
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2689
62.5%
Uppercase Letter 1613
37.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 1265
78.4%
L 113
 
7.0%
Q 113
 
7.0%
R 56
 
3.5%
B 33
 
2.0%
A 19
 
1.2%
G 14
 
0.9%
Lowercase Letter
ValueCountFrequency (%)
n 1265
47.0%
f 1265
47.0%
e 56
 
2.1%
c 56
 
2.1%
w 47
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 4302
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 1265
29.4%
n 1265
29.4%
f 1265
29.4%
L 113
 
2.6%
Q 113
 
2.6%
R 56
 
1.3%
e 56
 
1.3%
c 56
 
1.3%
w 47
 
1.1%
B 33
 
0.8%
Other values (2) 33
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4302
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U 1265
29.4%
n 1265
29.4%
f 1265
29.4%
L 113
 
2.6%
Q 113
 
2.6%
R 56
 
1.3%
e 56
 
1.3%
c 56
 
1.3%
w 47
 
1.1%
B 33
 
0.8%
Other values (2) 33
 
0.8%

BsmtFinSF2
Real number (ℝ)

Zeros 

Distinct144
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.17663043
Minimum0
Maximum1474
Zeros1302
Zeros (%)88.5%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-02-08T04:59:10.603598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile397.8
Maximum1474
Range1474
Interquartile range (IQR)0

Descriptive statistics

Standard deviation162.8807667
Coefficient of variation (CV)3.452573133
Kurtosis19.89880018
Mean47.17663043
Median Absolute Deviation (MAD)0
Skewness4.240469929
Sum69444
Variance26530.14417
MonotonicityNot monotonic
2025-02-08T04:59:10.769763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1302
88.5%
180 5
 
0.3%
290 3
 
0.2%
374 3
 
0.2%
551 2
 
0.1%
147 2
 
0.1%
294 2
 
0.1%
391 2
 
0.1%
539 2
 
0.1%
96 2
 
0.1%
Other values (134) 147
 
10.0%
ValueCountFrequency (%)
0 1302
88.5%
28 1
 
0.1%
32 1
 
0.1%
35 1
 
0.1%
40 1
 
0.1%
ValueCountFrequency (%)
1474 1
0.1%
1127 1
0.1%
1120 1
0.1%
1085 1
0.1%
1080 1
0.1%

BsmtUnfSF
Real number (ℝ)

Zeros 

Distinct780
Distinct (%)53.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean567.7873641
Minimum0
Maximum2336
Zeros120
Zeros (%)8.2%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-02-08T04:59:10.992742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1222.5
median480.5
Q3811
95-th percentile1468
Maximum2336
Range2336
Interquartile range (IQR)588.5

Descriptive statistics

Standard deviation442.2351366
Coefficient of variation (CV)0.7788745655
Kurtosis0.4568646005
Mean567.7873641
Median Absolute Deviation (MAD)290
Skewness0.9128920087
Sum835783
Variance195571.916
MonotonicityNot monotonic
2025-02-08T04:59:11.196659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 120
 
8.2%
728 9
 
0.6%
384 8
 
0.5%
300 7
 
0.5%
600 7
 
0.5%
572 7
 
0.5%
625 6
 
0.4%
440 6
 
0.4%
280 6
 
0.4%
270 6
 
0.4%
Other values (770) 1290
87.6%
ValueCountFrequency (%)
0 120
8.2%
14 1
 
0.1%
15 1
 
0.1%
23 2
 
0.1%
26 1
 
0.1%
ValueCountFrequency (%)
2336 1
0.1%
2153 1
0.1%
2121 1
0.1%
2046 1
0.1%
2042 1
0.1%

TotalBsmtSF
Real number (ℝ)

Zeros 

Distinct721
Distinct (%)49.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1057.33356
Minimum0
Maximum6110
Zeros37
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-02-08T04:59:11.362322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile520
Q1795.75
median992
Q31297.25
95-th percentile1752.45
Maximum6110
Range6110
Interquartile range (IQR)501.5

Descriptive statistics

Standard deviation437.9306751
Coefficient of variation (CV)0.4141840302
Kurtosis13.23850495
Mean1057.33356
Median Absolute Deviation (MAD)234.5
Skewness1.519889503
Sum1556395
Variance191783.2762
MonotonicityNot monotonic
2025-02-08T04:59:11.548208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 37
 
2.5%
864 35
 
2.4%
672 17
 
1.2%
912 15
 
1.0%
1040 14
 
1.0%
816 13
 
0.9%
728 12
 
0.8%
768 12
 
0.8%
894 11
 
0.7%
848 11
 
0.7%
Other values (711) 1295
88.0%
ValueCountFrequency (%)
0 37
2.5%
105 1
 
0.1%
190 1
 
0.1%
264 3
 
0.2%
270 1
 
0.1%
ValueCountFrequency (%)
6110 1
0.1%
3206 1
0.1%
3200 1
0.1%
3138 1
0.1%
3094 1
0.1%

Heating
Text

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
2025-02-08T04:59:11.713494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.000679348
Min length4

Characters and Unicode

Total characters5889
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowGasA
2nd rowGasA
3rd rowGasA
4th rowGasA
5th rowGasA
ValueCountFrequency (%)
gasa 1440
97.8%
gasw 18
 
1.2%
grav 7
 
0.5%
wall 4
 
0.3%
othw 2
 
0.1%
floor 1
 
0.1%
2025-02-08T04:59:12.042849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1469
24.9%
G 1465
24.9%
s 1458
24.8%
A 1440
24.5%
W 24
 
0.4%
l 9
 
0.2%
r 8
 
0.1%
v 7
 
0.1%
O 2
 
< 0.1%
t 2
 
< 0.1%
Other values (3) 5
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2957
50.2%
Uppercase Letter 2932
49.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1469
49.7%
s 1458
49.3%
l 9
 
0.3%
r 8
 
0.3%
v 7
 
0.2%
t 2
 
0.1%
h 2
 
0.1%
o 2
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
G 1465
50.0%
A 1440
49.1%
W 24
 
0.8%
O 2
 
0.1%
F 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 5889
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1469
24.9%
G 1465
24.9%
s 1458
24.8%
A 1440
24.5%
W 24
 
0.4%
l 9
 
0.2%
r 8
 
0.1%
v 7
 
0.1%
O 2
 
< 0.1%
t 2
 
< 0.1%
Other values (3) 5
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5889
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1469
24.9%
G 1465
24.9%
s 1458
24.8%
A 1440
24.5%
W 24
 
0.4%
l 9
 
0.2%
r 8
 
0.1%
v 7
 
0.1%
O 2
 
< 0.1%
t 2
 
< 0.1%
Other values (3) 5
 
0.1%

HeatingQC
Text

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
2025-02-08T04:59:12.160653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2944
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowEx
2nd rowEx
3rd rowEx
4th rowGd
5th rowEx
ValueCountFrequency (%)
ex 747
50.7%
ta 430
29.2%
gd 245
 
16.6%
fa 49
 
3.3%
po 1
 
0.1%
2025-02-08T04:59:12.442784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 747
25.4%
x 747
25.4%
T 430
14.6%
A 430
14.6%
G 245
 
8.3%
d 245
 
8.3%
F 49
 
1.7%
a 49
 
1.7%
P 1
 
< 0.1%
o 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1902
64.6%
Lowercase Letter 1042
35.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 747
39.3%
T 430
22.6%
A 430
22.6%
G 245
 
12.9%
F 49
 
2.6%
P 1
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
x 747
71.7%
d 245
 
23.5%
a 49
 
4.7%
o 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 2944
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 747
25.4%
x 747
25.4%
T 430
14.6%
A 430
14.6%
G 245
 
8.3%
d 245
 
8.3%
F 49
 
1.7%
a 49
 
1.7%
P 1
 
< 0.1%
o 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2944
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 747
25.4%
x 747
25.4%
T 430
14.6%
A 430
14.6%
G 245
 
8.3%
d 245
 
8.3%
F 49
 
1.7%
a 49
 
1.7%
P 1
 
< 0.1%
o 1
 
< 0.1%

CentralAir
Text

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
2025-02-08T04:59:12.546178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1472
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowY
2nd rowY
3rd rowY
4th rowY
5th rowY
ValueCountFrequency (%)
y 1376
93.5%
n 96
 
6.5%
2025-02-08T04:59:12.885807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
Y 1376
93.5%
N 96
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1472
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
Y 1376
93.5%
N 96
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 1472
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
Y 1376
93.5%
N 96
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1472
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
Y 1376
93.5%
N 96
 
6.5%

Electrical
Text

Distinct5
Distinct (%)0.3%
Missing1
Missing (%)0.1%
Memory size11.6 KiB
2025-02-08T04:59:13.061982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.998640381
Min length3

Characters and Unicode

Total characters7353
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowSBrkr
2nd rowSBrkr
3rd rowSBrkr
4th rowSBrkr
5th rowSBrkr
ValueCountFrequency (%)
sbrkr 1345
91.4%
fusea 95
 
6.5%
fusef 27
 
1.8%
fusep 3
 
0.2%
mix 1
 
0.1%
2025-02-08T04:59:13.369420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 2690
36.6%
S 1345
18.3%
B 1345
18.3%
k 1345
18.3%
F 152
 
2.1%
u 125
 
1.7%
s 125
 
1.7%
e 125
 
1.7%
A 95
 
1.3%
P 3
 
< 0.1%
Other values (3) 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4412
60.0%
Uppercase Letter 2941
40.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 2690
61.0%
k 1345
30.5%
u 125
 
2.8%
s 125
 
2.8%
e 125
 
2.8%
i 1
 
< 0.1%
x 1
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
S 1345
45.7%
B 1345
45.7%
F 152
 
5.2%
A 95
 
3.2%
P 3
 
0.1%
M 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 7353
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 2690
36.6%
S 1345
18.3%
B 1345
18.3%
k 1345
18.3%
F 152
 
2.1%
u 125
 
1.7%
s 125
 
1.7%
e 125
 
1.7%
A 95
 
1.3%
P 3
 
< 0.1%
Other values (3) 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 2690
36.6%
S 1345
18.3%
B 1345
18.3%
k 1345
18.3%
F 152
 
2.1%
u 125
 
1.7%
s 125
 
1.7%
e 125
 
1.7%
A 95
 
1.3%
P 3
 
< 0.1%
Other values (3) 3
 
< 0.1%

1stFlrSF
Real number (ℝ)

Distinct753
Distinct (%)51.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1162.578804
Minimum334
Maximum4692
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-02-08T04:59:13.552083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum334
5-th percentile672.55
Q1882
median1087
Q31391
95-th percentile1833.25
Maximum4692
Range4358
Interquartile range (IQR)509

Descriptive statistics

Standard deviation386.3852007
Coefficient of variation (CV)0.3323518365
Kurtosis5.718288269
Mean1162.578804
Median Absolute Deviation (MAD)233
Skewness1.375079621
Sum1711316
Variance149293.5234
MonotonicityNot monotonic
2025-02-08T04:59:13.732905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
864 25
 
1.7%
1040 16
 
1.1%
912 14
 
1.0%
848 12
 
0.8%
894 12
 
0.8%
672 11
 
0.7%
630 10
 
0.7%
816 9
 
0.6%
483 7
 
0.5%
832 7
 
0.5%
Other values (743) 1349
91.6%
ValueCountFrequency (%)
334 1
 
0.1%
372 1
 
0.1%
438 1
 
0.1%
480 1
 
0.1%
483 7
0.5%
ValueCountFrequency (%)
4692 1
0.1%
3228 1
0.1%
3138 1
0.1%
2898 1
0.1%
2633 1
0.1%

2ndFlrSF
Real number (ℝ)

Zeros 

Distinct417
Distinct (%)28.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean346.4001359
Minimum0
Maximum2065
Zeros837
Zeros (%)56.9%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-02-08T04:59:13.934366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3728
95-th percentile1141.45
Maximum2065
Range2065
Interquartile range (IQR)728

Descriptive statistics

Standard deviation436.36263
Coefficient of variation (CV)1.259706867
Kurtosis-0.5532744224
Mean346.4001359
Median Absolute Deviation (MAD)0
Skewness0.8146879965
Sum509901
Variance190412.3449
MonotonicityNot monotonic
2025-02-08T04:59:14.348649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 837
56.9%
728 10
 
0.7%
504 9
 
0.6%
546 8
 
0.5%
672 8
 
0.5%
896 7
 
0.5%
720 7
 
0.5%
600 7
 
0.5%
756 5
 
0.3%
862 5
 
0.3%
Other values (407) 569
38.7%
ValueCountFrequency (%)
0 837
56.9%
110 1
 
0.1%
167 1
 
0.1%
192 1
 
0.1%
208 1
 
0.1%
ValueCountFrequency (%)
2065 1
0.1%
1872 1
0.1%
1818 1
0.1%
1796 1
0.1%
1611 1
0.1%

LowQualFinSF
Real number (ℝ)

Zeros 

Distinct24
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.796875
Minimum0
Maximum572
Zeros1446
Zeros (%)98.2%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-02-08T04:59:14.513010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum572
Range572
Interquartile range (IQR)0

Descriptive statistics

Standard deviation48.42720234
Coefficient of variation (CV)8.354018732
Kurtosis83.95648057
Mean5.796875
Median Absolute Deviation (MAD)0
Skewness9.04956428
Sum8533
Variance2345.193926
MonotonicityNot monotonic
2025-02-08T04:59:14.678811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 1446
98.2%
80 3
 
0.2%
360 2
 
0.1%
205 1
 
0.1%
479 1
 
0.1%
397 1
 
0.1%
514 1
 
0.1%
120 1
 
0.1%
481 1
 
0.1%
232 1
 
0.1%
Other values (14) 14
 
1.0%
ValueCountFrequency (%)
0 1446
98.2%
53 1
 
0.1%
80 3
 
0.2%
120 1
 
0.1%
144 1
 
0.1%
ValueCountFrequency (%)
572 1
0.1%
528 1
0.1%
515 1
0.1%
514 1
0.1%
513 1
0.1%

GrLivArea
Real number (ℝ)

Distinct861
Distinct (%)58.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1514.775815
Minimum334
Maximum5642
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-02-08T04:59:14.935683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum334
5-th percentile848
Q11129.5
median1461.5
Q31776.75
95-th percentile2463.8
Maximum5642
Range5308
Interquartile range (IQR)647.25

Descriptive statistics

Standard deviation525.0079356
Coefficient of variation (CV)0.3465911789
Kurtosis4.877415225
Mean1514.775815
Median Absolute Deviation (MAD)326
Skewness1.362015179
Sum2229750
Variance275633.3324
MonotonicityNot monotonic
2025-02-08T04:59:15.131527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
864 22
 
1.5%
1040 14
 
1.0%
894 11
 
0.7%
1456 10
 
0.7%
848 10
 
0.7%
912 9
 
0.6%
1200 9
 
0.6%
1092 8
 
0.5%
816 8
 
0.5%
630 7
 
0.5%
Other values (851) 1364
92.7%
ValueCountFrequency (%)
334 1
0.1%
438 1
0.1%
480 1
0.1%
520 1
0.1%
605 1
0.1%
ValueCountFrequency (%)
5642 1
0.1%
4676 1
0.1%
4476 1
0.1%
4316 1
0.1%
3627 1
0.1%

BsmtFullBath
Real number (ℝ)

Zeros 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.425951087
Minimum0
Maximum3
Zeros862
Zeros (%)58.6%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-02-08T04:59:15.266532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum3
Range3
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5188025933
Coefficient of variation (CV)1.217986312
Kurtosis-0.8497831229
Mean0.425951087
Median Absolute Deviation (MAD)0
Skewness0.5913425364
Sum627
Variance0.2691561308
MonotonicityNot monotonic
2025-02-08T04:59:15.413650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
0 862
58.6%
1 594
40.4%
2 15
 
1.0%
3 1
 
0.1%
ValueCountFrequency (%)
0 862
58.6%
1 594
40.4%
2 15
 
1.0%
3 1
 
0.1%
ValueCountFrequency (%)
3 1
 
0.1%
2 15
 
1.0%
1 594
40.4%
0 862
58.6%

BsmtHalfBath
Real number (ℝ)

Zeros 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.05706521739
Minimum0
Maximum2
Zeros1390
Zeros (%)94.4%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-02-08T04:59:15.551543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum2
Range2
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2378331278
Coefficient of variation (CV)4.167742431
Kurtosis16.56971761
Mean0.05706521739
Median Absolute Deviation (MAD)0
Skewness4.123079583
Sum84
Variance0.0565645967
MonotonicityNot monotonic
2025-02-08T04:59:15.691102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
0 1390
94.4%
1 80
 
5.4%
2 2
 
0.1%
ValueCountFrequency (%)
0 1390
94.4%
1 80
 
5.4%
2 2
 
0.1%
ValueCountFrequency (%)
2 2
 
0.1%
1 80
 
5.4%
0 1390
94.4%

FullBath
Real number (ℝ)

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.564538043
Minimum0
Maximum3
Zeros9
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-02-08T04:59:15.804852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q32
95-th percentile2
Maximum3
Range3
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5505506087
Coefficient of variation (CV)0.3518933982
Kurtosis-0.862657888
Mean1.564538043
Median Absolute Deviation (MAD)0
Skewness0.03682711232
Sum2303
Variance0.3031059727
MonotonicityNot monotonic
2025-02-08T04:59:15.934869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
2 774
52.6%
1 656
44.6%
3 33
 
2.2%
0 9
 
0.6%
ValueCountFrequency (%)
0 9
 
0.6%
1 656
44.6%
2 774
52.6%
3 33
 
2.2%
ValueCountFrequency (%)
3 33
 
2.2%
2 774
52.6%
1 656
44.6%
0 9
 
0.6%

HalfBath
Real number (ℝ)

Zeros 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3831521739
Minimum0
Maximum2
Zeros921
Zeros (%)62.6%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-02-08T04:59:16.038867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum2
Range2
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5041649603
Coefficient of variation (CV)1.31583479
Kurtosis-1.034875435
Mean0.3831521739
Median Absolute Deviation (MAD)0
Skewness0.6874677802
Sum564
Variance0.2541823072
MonotonicityNot monotonic
2025-02-08T04:59:16.181976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
0 921
62.6%
1 538
36.5%
2 13
 
0.9%
ValueCountFrequency (%)
0 921
62.6%
1 538
36.5%
2 13
 
0.9%
ValueCountFrequency (%)
2 13
 
0.9%
1 538
36.5%
0 921
62.6%

BedroomAbvGr
Real number (ℝ)

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.864809783
Minimum0
Maximum8
Zeros6
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-02-08T04:59:16.349216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median3
Q33
95-th percentile4
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8162594572
Coefficient of variation (CV)0.2849262322
Kurtosis2.204631496
Mean2.864809783
Median Absolute Deviation (MAD)0
Skewness0.2079485695
Sum4217
Variance0.6662795015
MonotonicityNot monotonic
2025-02-08T04:59:16.496636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3 809
55.0%
2 362
24.6%
4 215
 
14.6%
1 51
 
3.5%
5 21
 
1.4%
6 7
 
0.5%
0 6
 
0.4%
8 1
 
0.1%
ValueCountFrequency (%)
0 6
 
0.4%
1 51
 
3.5%
2 362
24.6%
3 809
55.0%
4 215
 
14.6%
ValueCountFrequency (%)
8 1
 
0.1%
6 7
 
0.5%
5 21
 
1.4%
4 215
 
14.6%
3 809
55.0%

KitchenAbvGr
Real number (ℝ)

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.046875
Minimum0
Maximum3
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-02-08T04:59:16.652975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum3
Range3
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2208777142
Coefficient of variation (CV)0.2109876673
Kurtosis21.30719435
Mean1.046875
Median Absolute Deviation (MAD)0
Skewness4.469794035
Sum1541
Variance0.04878696465
MonotonicityNot monotonic
2025-02-08T04:59:16.824204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
1 1403
95.3%
2 66
 
4.5%
3 2
 
0.1%
0 1
 
0.1%
ValueCountFrequency (%)
0 1
 
0.1%
1 1403
95.3%
2 66
 
4.5%
3 2
 
0.1%
ValueCountFrequency (%)
3 2
 
0.1%
2 66
 
4.5%
1 1403
95.3%
0 1
 
0.1%

KitchenQual
Text

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
2025-02-08T04:59:16.979387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2944
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGd
2nd rowTA
3rd rowGd
4th rowGd
5th rowGd
ValueCountFrequency (%)
ta 742
50.4%
gd 589
40.0%
ex 102
 
6.9%
fa 39
 
2.6%
2025-02-08T04:59:17.356016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T 742
25.2%
A 742
25.2%
G 589
20.0%
d 589
20.0%
E 102
 
3.5%
x 102
 
3.5%
F 39
 
1.3%
a 39
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2214
75.2%
Lowercase Letter 730
 
24.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 742
33.5%
A 742
33.5%
G 589
26.6%
E 102
 
4.6%
F 39
 
1.8%
Lowercase Letter
ValueCountFrequency (%)
d 589
80.7%
x 102
 
14.0%
a 39
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 2944
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 742
25.2%
A 742
25.2%
G 589
20.0%
d 589
20.0%
E 102
 
3.5%
x 102
 
3.5%
F 39
 
1.3%
a 39
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2944
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 742
25.2%
A 742
25.2%
G 589
20.0%
d 589
20.0%
E 102
 
3.5%
x 102
 
3.5%
F 39
 
1.3%
a 39
 
1.3%

TotRmsAbvGrd
Real number (ℝ)

Distinct12
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.515625
Minimum2
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-02-08T04:59:17.532182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q15
median6
Q37
95-th percentile10
Maximum14
Range12
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.624431214
Coefficient of variation (CV)0.2493131839
Kurtosis0.88201209
Mean6.515625
Median Absolute Deviation (MAD)1
Skewness0.670933082
Sum9591
Variance2.638776768
MonotonicityNot monotonic
2025-02-08T04:59:17.674246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6 406
27.6%
7 332
22.6%
5 277
18.8%
8 188
12.8%
4 97
 
6.6%
9 76
 
5.2%
10 47
 
3.2%
11 18
 
1.2%
3 18
 
1.2%
12 11
 
0.7%
Other values (2) 2
 
0.1%
ValueCountFrequency (%)
2 1
 
0.1%
3 18
 
1.2%
4 97
 
6.6%
5 277
18.8%
6 406
27.6%
ValueCountFrequency (%)
14 1
 
0.1%
12 11
 
0.7%
11 18
 
1.2%
10 47
3.2%
9 76
5.2%

Functional
Text

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
2025-02-08T04:59:17.784976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.058423913
Min length3

Characters and Unicode

Total characters4502
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowTyp
2nd rowTyp
3rd rowTyp
4th rowTyp
5th rowTyp
ValueCountFrequency (%)
typ 1370
93.1%
min2 35
 
2.4%
min1 32
 
2.2%
mod 15
 
1.0%
maj1 14
 
1.0%
maj2 5
 
0.3%
sev 1
 
0.1%
2025-02-08T04:59:18.115447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T 1370
30.4%
y 1370
30.4%
p 1370
30.4%
M 101
 
2.2%
i 67
 
1.5%
n 67
 
1.5%
1 46
 
1.0%
2 40
 
0.9%
a 19
 
0.4%
j 19
 
0.4%
Other values (5) 33
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2944
65.4%
Uppercase Letter 1472
32.7%
Decimal Number 86
 
1.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
y 1370
46.5%
p 1370
46.5%
i 67
 
2.3%
n 67
 
2.3%
a 19
 
0.6%
j 19
 
0.6%
o 15
 
0.5%
d 15
 
0.5%
e 1
 
< 0.1%
v 1
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
T 1370
93.1%
M 101
 
6.9%
S 1
 
0.1%
Decimal Number
ValueCountFrequency (%)
1 46
53.5%
2 40
46.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 4416
98.1%
Common 86
 
1.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 1370
31.0%
y 1370
31.0%
p 1370
31.0%
M 101
 
2.3%
i 67
 
1.5%
n 67
 
1.5%
a 19
 
0.4%
j 19
 
0.4%
o 15
 
0.3%
d 15
 
0.3%
Other values (3) 3
 
0.1%
Common
ValueCountFrequency (%)
1 46
53.5%
2 40
46.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4502
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 1370
30.4%
y 1370
30.4%
p 1370
30.4%
M 101
 
2.2%
i 67
 
1.5%
n 67
 
1.5%
1 46
 
1.0%
2 40
 
0.9%
a 19
 
0.4%
j 19
 
0.4%
Other values (5) 33
 
0.7%

Fireplaces
Real number (ℝ)

Zeros 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6120923913
Minimum0
Maximum3
Zeros698
Zeros (%)47.4%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-02-08T04:59:18.225872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile2
Maximum3
Range3
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.6458068249
Coefficient of variation (CV)1.055080628
Kurtosis-0.2221326574
Mean0.6120923913
Median Absolute Deviation (MAD)1
Skewness0.6542598308
Sum901
Variance0.4170664551
MonotonicityNot monotonic
2025-02-08T04:59:18.369579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
0 698
47.4%
1 652
44.3%
2 117
 
7.9%
3 5
 
0.3%
ValueCountFrequency (%)
0 698
47.4%
1 652
44.3%
2 117
 
7.9%
3 5
 
0.3%
ValueCountFrequency (%)
3 5
 
0.3%
2 117
 
7.9%
1 652
44.3%
0 698
47.4%

FireplaceQu
Text

Missing 

Distinct5
Distinct (%)0.6%
Missing698
Missing (%)47.4%
Memory size11.6 KiB
2025-02-08T04:59:18.509300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1548
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTA
2nd rowTA
3rd rowGd
4th rowTA
5th rowGd
ValueCountFrequency (%)
gd 382
49.4%
ta 315
40.7%
fa 33
 
4.3%
ex 24
 
3.1%
po 20
 
2.6%
2025-02-08T04:59:18.781647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
G 382
24.7%
d 382
24.7%
T 315
20.3%
A 315
20.3%
F 33
 
2.1%
a 33
 
2.1%
E 24
 
1.6%
x 24
 
1.6%
P 20
 
1.3%
o 20
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1089
70.3%
Lowercase Letter 459
29.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G 382
35.1%
T 315
28.9%
A 315
28.9%
F 33
 
3.0%
E 24
 
2.2%
P 20
 
1.8%
Lowercase Letter
ValueCountFrequency (%)
d 382
83.2%
a 33
 
7.2%
x 24
 
5.2%
o 20
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 1548
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
G 382
24.7%
d 382
24.7%
T 315
20.3%
A 315
20.3%
F 33
 
2.1%
a 33
 
2.1%
E 24
 
1.6%
x 24
 
1.6%
P 20
 
1.3%
o 20
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1548
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
G 382
24.7%
d 382
24.7%
T 315
20.3%
A 315
20.3%
F 33
 
2.1%
a 33
 
2.1%
E 24
 
1.6%
x 24
 
1.6%
P 20
 
1.3%
o 20
 
1.3%

GarageType
Text

Missing 

Distinct6
Distinct (%)0.4%
Missing84
Missing (%)5.7%
Memory size11.6 KiB
2025-02-08T04:59:18.931914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.084293948
Min length6

Characters and Unicode

Total characters8445
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAttchd
2nd rowAttchd
3rd rowAttchd
4th rowDetchd
5th rowAttchd
ValueCountFrequency (%)
attchd 877
63.2%
detchd 388
28.0%
builtin 88
 
6.3%
basment 20
 
1.4%
carport 9
 
0.6%
2types 6
 
0.4%
2025-02-08T04:59:19.292684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 2259
26.7%
c 1265
15.0%
h 1265
15.0%
d 1265
15.0%
A 877
 
10.4%
e 414
 
4.9%
D 388
 
4.6%
n 108
 
1.3%
B 108
 
1.3%
u 88
 
1.0%
Other values (14) 408
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6954
82.3%
Uppercase Letter 1485
 
17.6%
Decimal Number 6
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 2259
32.5%
c 1265
18.2%
h 1265
18.2%
d 1265
18.2%
e 414
 
6.0%
n 108
 
1.6%
u 88
 
1.3%
i 88
 
1.3%
l 88
 
1.3%
a 29
 
0.4%
Other values (6) 85
 
1.2%
Uppercase Letter
ValueCountFrequency (%)
A 877
59.1%
D 388
26.1%
B 108
 
7.3%
I 88
 
5.9%
C 9
 
0.6%
P 9
 
0.6%
T 6
 
0.4%
Decimal Number
ValueCountFrequency (%)
2 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8439
99.9%
Common 6
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 2259
26.8%
c 1265
15.0%
h 1265
15.0%
d 1265
15.0%
A 877
 
10.4%
e 414
 
4.9%
D 388
 
4.6%
n 108
 
1.3%
B 108
 
1.3%
u 88
 
1.0%
Other values (13) 402
 
4.8%
Common
ValueCountFrequency (%)
2 6
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8445
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 2259
26.7%
c 1265
15.0%
h 1265
15.0%
d 1265
15.0%
A 877
 
10.4%
e 414
 
4.9%
D 388
 
4.6%
n 108
 
1.3%
B 108
 
1.3%
u 88
 
1.0%
Other values (14) 408
 
4.8%

GarageYrBlt
Real number (ℝ)

Missing 

Distinct97
Distinct (%)7.0%
Missing84
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean1978.501441
Minimum1900
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-02-08T04:59:19.500888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1930
Q11961
median1980
Q32002
95-th percentile2007
Maximum2010
Range110
Interquartile range (IQR)41

Descriptive statistics

Standard deviation24.69512731
Coefficient of variation (CV)0.0124817333
Kurtosis-0.4278233831
Mean1978.501441
Median Absolute Deviation (MAD)21
Skewness-0.6457367112
Sum2746160
Variance609.849313
MonotonicityNot monotonic
2025-02-08T04:59:19.733916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2005 66
 
4.5%
2006 59
 
4.0%
2004 54
 
3.7%
2003 50
 
3.4%
2007 49
 
3.3%
1977 35
 
2.4%
1999 31
 
2.1%
1998 31
 
2.1%
2008 30
 
2.0%
1976 29
 
2.0%
Other values (87) 954
64.8%
(Missing) 84
 
5.7%
ValueCountFrequency (%)
1900 1
 
0.1%
1906 1
 
0.1%
1908 1
 
0.1%
1910 3
0.2%
1914 2
0.1%
ValueCountFrequency (%)
2010 3
 
0.2%
2009 21
 
1.4%
2008 30
2.0%
2007 49
3.3%
2006 59
4.0%

GarageFinish
Text

Missing 

Distinct3
Distinct (%)0.2%
Missing84
Missing (%)5.7%
Memory size11.6 KiB
2025-02-08T04:59:19.867112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4164
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRFn
2nd rowRFn
3rd rowRFn
4th rowUnf
5th rowRFn
ValueCountFrequency (%)
unf 608
43.8%
rfn 425
30.6%
fin 355
25.6%
2025-02-08T04:59:20.162576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 1388
33.3%
F 780
18.7%
U 608
14.6%
f 608
14.6%
R 425
 
10.2%
i 355
 
8.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2351
56.5%
Uppercase Letter 1813
43.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 1388
59.0%
f 608
25.9%
i 355
 
15.1%
Uppercase Letter
ValueCountFrequency (%)
F 780
43.0%
U 608
33.5%
R 425
23.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 4164
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 1388
33.3%
F 780
18.7%
U 608
14.6%
f 608
14.6%
R 425
 
10.2%
i 355
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4164
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 1388
33.3%
F 780
18.7%
U 608
14.6%
f 608
14.6%
R 425
 
10.2%
i 355
 
8.5%

GarageCars
Real number (ℝ)

Zeros 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.762907609
Minimum0
Maximum4
Zeros84
Zeros (%)5.7%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-02-08T04:59:20.328620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q32
95-th percentile3
Maximum4
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.750370294
Coefficient of variation (CV)0.4256435733
Kurtosis0.2058011051
Mean1.762907609
Median Absolute Deviation (MAD)0
Skewness-0.3452217482
Sum2595
Variance0.5630555781
MonotonicityNot monotonic
2025-02-08T04:59:20.465736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
2 828
56.2%
1 373
25.3%
3 182
 
12.4%
0 84
 
5.7%
4 5
 
0.3%
ValueCountFrequency (%)
0 84
 
5.7%
1 373
25.3%
2 828
56.2%
3 182
 
12.4%
4 5
 
0.3%
ValueCountFrequency (%)
4 5
 
0.3%
3 182
 
12.4%
2 828
56.2%
1 373
25.3%
0 84
 
5.7%

GarageArea
Real number (ℝ)

Zeros 

Distinct441
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean471.7581522
Minimum0
Maximum1418
Zeros84
Zeros (%)5.7%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-02-08T04:59:20.642149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1326.75
median478
Q3576
95-th percentile847.8
Maximum1418
Range1418
Interquartile range (IQR)249.25

Descriptive statistics

Standard deviation214.4617627
Coefficient of variation (CV)0.4546010741
Kurtosis0.8978280831
Mean471.7581522
Median Absolute Deviation (MAD)118
Skewness0.1732881935
Sum694428
Variance45993.84766
MonotonicityNot monotonic
2025-02-08T04:59:20.846854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 84
 
5.7%
440 49
 
3.3%
576 47
 
3.2%
240 39
 
2.6%
484 34
 
2.3%
528 33
 
2.2%
288 27
 
1.8%
400 26
 
1.8%
480 24
 
1.6%
264 24
 
1.6%
Other values (431) 1085
73.7%
ValueCountFrequency (%)
0 84
5.7%
160 2
 
0.1%
164 1
 
0.1%
180 9
 
0.6%
186 1
 
0.1%
ValueCountFrequency (%)
1418 1
0.1%
1390 1
0.1%
1356 1
0.1%
1248 1
0.1%
1220 1
0.1%

GarageQual
Text

Missing 

Distinct5
Distinct (%)0.4%
Missing84
Missing (%)5.7%
Memory size11.6 KiB
2025-02-08T04:59:20.993747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2776
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTA
2nd rowTA
3rd rowTA
4th rowTA
5th rowTA
ValueCountFrequency (%)
ta 1319
95.0%
fa 49
 
3.5%
gd 14
 
1.0%
ex 3
 
0.2%
po 3
 
0.2%
2025-02-08T04:59:21.305974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T 1319
47.5%
A 1319
47.5%
F 49
 
1.8%
a 49
 
1.8%
G 14
 
0.5%
d 14
 
0.5%
E 3
 
0.1%
x 3
 
0.1%
P 3
 
0.1%
o 3
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2707
97.5%
Lowercase Letter 69
 
2.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 1319
48.7%
A 1319
48.7%
F 49
 
1.8%
G 14
 
0.5%
E 3
 
0.1%
P 3
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
a 49
71.0%
d 14
 
20.3%
x 3
 
4.3%
o 3
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 2776
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 1319
47.5%
A 1319
47.5%
F 49
 
1.8%
a 49
 
1.8%
G 14
 
0.5%
d 14
 
0.5%
E 3
 
0.1%
x 3
 
0.1%
P 3
 
0.1%
o 3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2776
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 1319
47.5%
A 1319
47.5%
F 49
 
1.8%
a 49
 
1.8%
G 14
 
0.5%
d 14
 
0.5%
E 3
 
0.1%
x 3
 
0.1%
P 3
 
0.1%
o 3
 
0.1%

GarageCond
Text

Missing 

Distinct5
Distinct (%)0.4%
Missing84
Missing (%)5.7%
Memory size11.6 KiB
2025-02-08T04:59:21.442258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2776
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTA
2nd rowTA
3rd rowTA
4th rowTA
5th rowTA
ValueCountFrequency (%)
ta 1335
96.2%
fa 35
 
2.5%
gd 9
 
0.6%
po 7
 
0.5%
ex 2
 
0.1%
2025-02-08T04:59:21.671734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T 1335
48.1%
A 1335
48.1%
F 35
 
1.3%
a 35
 
1.3%
G 9
 
0.3%
d 9
 
0.3%
P 7
 
0.3%
o 7
 
0.3%
E 2
 
0.1%
x 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2723
98.1%
Lowercase Letter 53
 
1.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 1335
49.0%
A 1335
49.0%
F 35
 
1.3%
G 9
 
0.3%
P 7
 
0.3%
E 2
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
a 35
66.0%
d 9
 
17.0%
o 7
 
13.2%
x 2
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 2776
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 1335
48.1%
A 1335
48.1%
F 35
 
1.3%
a 35
 
1.3%
G 9
 
0.3%
d 9
 
0.3%
P 7
 
0.3%
o 7
 
0.3%
E 2
 
0.1%
x 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2776
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 1335
48.1%
A 1335
48.1%
F 35
 
1.3%
a 35
 
1.3%
G 9
 
0.3%
d 9
 
0.3%
P 7
 
0.3%
o 7
 
0.3%
E 2
 
0.1%
x 2
 
0.1%

PavedDrive
Text

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
2025-02-08T04:59:21.754683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1472
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowY
2nd rowY
3rd rowY
4th rowY
5th rowY
ValueCountFrequency (%)
y 1352
91.8%
n 90
 
6.1%
p 30
 
2.0%
2025-02-08T04:59:22.028885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
Y 1352
91.8%
N 90
 
6.1%
P 30
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1472
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
Y 1352
91.8%
N 90
 
6.1%
P 30
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1472
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
Y 1352
91.8%
N 90
 
6.1%
P 30
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1472
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
Y 1352
91.8%
N 90
 
6.1%
P 30
 
2.0%

WoodDeckSF
Real number (ℝ)

Zeros 

Distinct274
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.62228261
Minimum0
Maximum857
Zeros767
Zeros (%)52.1%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-02-08T04:59:22.286446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3168
95-th percentile337.25
Maximum857
Range857
Interquartile range (IQR)168

Descriptive statistics

Standard deviation126.493815
Coefficient of variation (CV)1.336829038
Kurtosis3.189962504
Mean94.62228261
Median Absolute Deviation (MAD)0
Skewness1.577820425
Sum139284
Variance16000.68524
MonotonicityNot monotonic
2025-02-08T04:59:22.528180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 767
52.1%
192 38
 
2.6%
100 36
 
2.4%
144 33
 
2.2%
120 31
 
2.1%
168 29
 
2.0%
140 15
 
1.0%
224 14
 
1.0%
208 10
 
0.7%
240 10
 
0.7%
Other values (264) 489
33.2%
ValueCountFrequency (%)
0 767
52.1%
12 2
 
0.1%
24 2
 
0.1%
26 2
 
0.1%
28 2
 
0.1%
ValueCountFrequency (%)
857 1
0.1%
736 2
0.1%
728 1
0.1%
670 1
0.1%
668 1
0.1%

OpenPorchSF
Real number (ℝ)

Zeros 

Distinct202
Distinct (%)13.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.59918478
Minimum0
Maximum547
Zeros659
Zeros (%)44.8%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-02-08T04:59:22.814402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median25
Q368
95-th percentile174.45
Maximum547
Range547
Interquartile range (IQR)68

Descriptive statistics

Standard deviation66.05045925
Coefficient of variation (CV)1.417416626
Kurtosis8.548845749
Mean46.59918478
Median Absolute Deviation (MAD)25
Skewness2.369723616
Sum68594
Variance4362.663167
MonotonicityNot monotonic
2025-02-08T04:59:23.003302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 659
44.8%
36 30
 
2.0%
48 22
 
1.5%
20 21
 
1.4%
40 20
 
1.4%
45 20
 
1.4%
24 17
 
1.2%
60 16
 
1.1%
30 16
 
1.1%
28 15
 
1.0%
Other values (192) 636
43.2%
ValueCountFrequency (%)
0 659
44.8%
4 1
 
0.1%
8 1
 
0.1%
10 1
 
0.1%
11 1
 
0.1%
ValueCountFrequency (%)
547 1
0.1%
523 1
0.1%
502 1
0.1%
418 1
0.1%
406 1
0.1%

EnclosedPorch
Real number (ℝ)

Zeros 

Distinct120
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.85122283
Minimum0
Maximum552
Zeros1263
Zeros (%)85.8%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-02-08T04:59:23.142958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile180
Maximum552
Range552
Interquartile range (IQR)0

Descriptive statistics

Standard deviation60.94409301
Coefficient of variation (CV)2.789047254
Kurtosis10.49887576
Mean21.85122283
Median Absolute Deviation (MAD)0
Skewness3.098032978
Sum32165
Variance3714.182473
MonotonicityNot monotonic
2025-02-08T04:59:23.317069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1263
85.8%
112 16
 
1.1%
96 6
 
0.4%
192 5
 
0.3%
144 5
 
0.3%
120 5
 
0.3%
216 5
 
0.3%
156 4
 
0.3%
116 4
 
0.3%
252 4
 
0.3%
Other values (110) 155
 
10.5%
ValueCountFrequency (%)
0 1263
85.8%
19 1
 
0.1%
20 1
 
0.1%
24 1
 
0.1%
30 1
 
0.1%
ValueCountFrequency (%)
552 1
0.1%
386 1
0.1%
330 1
0.1%
318 1
0.1%
301 1
0.1%

3SsnPorch
Real number (ℝ)

Zeros 

Distinct20
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.381793478
Minimum0
Maximum508
Zeros1448
Zeros (%)98.4%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-02-08T04:59:23.537078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum508
Range508
Interquartile range (IQR)0

Descriptive statistics

Standard deviation29.19911521
Coefficient of variation (CV)8.634210042
Kurtosis124.7112979
Mean3.381793478
Median Absolute Deviation (MAD)0
Skewness10.34764968
Sum4978
Variance852.5883291
MonotonicityNot monotonic
2025-02-08T04:59:23.662423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 1448
98.4%
168 3
 
0.2%
144 2
 
0.1%
180 2
 
0.1%
216 2
 
0.1%
290 1
 
0.1%
153 1
 
0.1%
96 1
 
0.1%
23 1
 
0.1%
162 1
 
0.1%
Other values (10) 10
 
0.7%
ValueCountFrequency (%)
0 1448
98.4%
23 1
 
0.1%
96 1
 
0.1%
130 1
 
0.1%
140 1
 
0.1%
ValueCountFrequency (%)
508 1
0.1%
407 1
0.1%
320 1
0.1%
304 1
0.1%
290 1
0.1%

ScreenPorch
Real number (ℝ)

Zeros 

Distinct76
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.93817935
Minimum0
Maximum480
Zeros1356
Zeros (%)92.1%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-02-08T04:59:24.018661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile160
Maximum480
Range480
Interquartile range (IQR)0

Descriptive statistics

Standard deviation55.54604642
Coefficient of variation (CV)3.718394667
Kurtosis18.62579653
Mean14.93817935
Median Absolute Deviation (MAD)0
Skewness4.141869687
Sum21989
Variance3085.363273
MonotonicityNot monotonic
2025-02-08T04:59:24.258601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1356
92.1%
192 6
 
0.4%
120 5
 
0.3%
224 5
 
0.3%
189 4
 
0.3%
180 4
 
0.3%
147 3
 
0.2%
90 3
 
0.2%
160 3
 
0.2%
144 3
 
0.2%
Other values (66) 80
 
5.4%
ValueCountFrequency (%)
0 1356
92.1%
40 1
 
0.1%
53 1
 
0.1%
60 1
 
0.1%
63 1
 
0.1%
ValueCountFrequency (%)
480 1
0.1%
440 1
0.1%
410 1
0.1%
396 1
0.1%
385 1
0.1%

PoolArea
Real number (ℝ)

Zeros 

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.736413043
Minimum0
Maximum738
Zeros1465
Zeros (%)99.5%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-02-08T04:59:24.407753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum738
Range738
Interquartile range (IQR)0

Descriptive statistics

Standard deviation40.01386341
Coefficient of variation (CV)14.62274254
Kurtosis225.1379799
Mean2.736413043
Median Absolute Deviation (MAD)0
Skewness14.88988936
Sum4028
Variance1601.109265
MonotonicityNot monotonic
2025-02-08T04:59:24.529645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 1465
99.5%
512 1
 
0.1%
648 1
 
0.1%
576 1
 
0.1%
555 1
 
0.1%
480 1
 
0.1%
519 1
 
0.1%
738 1
 
0.1%
ValueCountFrequency (%)
0 1465
99.5%
480 1
 
0.1%
512 1
 
0.1%
519 1
 
0.1%
555 1
 
0.1%
ValueCountFrequency (%)
738 1
0.1%
648 1
0.1%
576 1
0.1%
555 1
0.1%
519 1
0.1%

PoolQC
Text

Missing 

Distinct3
Distinct (%)42.9%
Missing1465
Missing (%)99.5%
Memory size11.6 KiB
2025-02-08T04:59:24.652266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters14
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEx
2nd rowFa
3rd rowGd
4th rowEx
5th rowGd
ValueCountFrequency (%)
gd 3
42.9%
ex 2
28.6%
fa 2
28.6%
2025-02-08T04:59:24.935425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
G 3
21.4%
d 3
21.4%
E 2
14.3%
x 2
14.3%
F 2
14.3%
a 2
14.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 7
50.0%
Lowercase Letter 7
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G 3
42.9%
E 2
28.6%
F 2
28.6%
Lowercase Letter
ValueCountFrequency (%)
d 3
42.9%
x 2
28.6%
a 2
28.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 14
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
G 3
21.4%
d 3
21.4%
E 2
14.3%
x 2
14.3%
F 2
14.3%
a 2
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
G 3
21.4%
d 3
21.4%
E 2
14.3%
x 2
14.3%
F 2
14.3%
a 2
14.3%

Fence
Text

Missing 

Distinct4
Distinct (%)1.4%
Missing1188
Missing (%)80.7%
Memory size11.6 KiB
2025-02-08T04:59:25.064409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.767605634
Min length4

Characters and Unicode

Total characters1354
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMnPrv
2nd rowGdWo
3rd rowGdPrv
4th rowMnPrv
5th rowGdPrv
ValueCountFrequency (%)
mnprv 158
55.6%
gdprv 60
 
21.1%
gdwo 55
 
19.4%
mnww 11
 
3.9%
2025-02-08T04:59:25.332925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
P 218
16.1%
r 218
16.1%
v 218
16.1%
M 169
12.5%
n 169
12.5%
G 115
8.5%
d 115
8.5%
W 66
 
4.9%
o 55
 
4.1%
w 11
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 786
58.1%
Uppercase Letter 568
41.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 218
27.7%
v 218
27.7%
n 169
21.5%
d 115
14.6%
o 55
 
7.0%
w 11
 
1.4%
Uppercase Letter
ValueCountFrequency (%)
P 218
38.4%
M 169
29.8%
G 115
20.2%
W 66
 
11.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 1354
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 218
16.1%
r 218
16.1%
v 218
16.1%
M 169
12.5%
n 169
12.5%
G 115
8.5%
d 115
8.5%
W 66
 
4.9%
o 55
 
4.1%
w 11
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1354
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 218
16.1%
r 218
16.1%
v 218
16.1%
M 169
12.5%
n 169
12.5%
G 115
8.5%
d 115
8.5%
W 66
 
4.9%
o 55
 
4.1%
w 11
 
0.8%

MiscFeature
Text

Missing 

Distinct4
Distinct (%)7.3%
Missing1417
Missing (%)96.3%
Memory size11.6 KiB
2025-02-08T04:59:25.465713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters220
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.8%

Sample

1st rowShed
2nd rowShed
3rd rowShed
4th rowShed
5th rowShed
ValueCountFrequency (%)
shed 50
90.9%
gar2 2
 
3.6%
othr 2
 
3.6%
tenc 1
 
1.8%
2025-02-08T04:59:25.808198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
h 52
23.6%
e 51
23.2%
S 50
22.7%
d 50
22.7%
r 4
 
1.8%
G 2
 
0.9%
a 2
 
0.9%
2 2
 
0.9%
O 2
 
0.9%
t 2
 
0.9%
Other values (3) 3
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 162
73.6%
Uppercase Letter 56
 
25.5%
Decimal Number 2
 
0.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
h 52
32.1%
e 51
31.5%
d 50
30.9%
r 4
 
2.5%
a 2
 
1.2%
t 2
 
1.2%
n 1
 
0.6%
Uppercase Letter
ValueCountFrequency (%)
S 50
89.3%
G 2
 
3.6%
O 2
 
3.6%
T 1
 
1.8%
C 1
 
1.8%
Decimal Number
ValueCountFrequency (%)
2 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 218
99.1%
Common 2
 
0.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
h 52
23.9%
e 51
23.4%
S 50
22.9%
d 50
22.9%
r 4
 
1.8%
G 2
 
0.9%
a 2
 
0.9%
O 2
 
0.9%
t 2
 
0.9%
T 1
 
0.5%
Other values (2) 2
 
0.9%
Common
ValueCountFrequency (%)
2 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 220
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
h 52
23.6%
e 51
23.2%
S 50
22.7%
d 50
22.7%
r 4
 
1.8%
G 2
 
0.9%
a 2
 
0.9%
2 2
 
0.9%
O 2
 
0.9%
t 2
 
0.9%
Other values (3) 3
 
1.4%

MiscVal
Real number (ℝ)

Skewed  Zeros 

Distinct21
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.83288043
Minimum0
Maximum15500
Zeros1419
Zeros (%)96.4%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-02-08T04:59:25.974492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum15500
Range15500
Interquartile range (IQR)0

Descriptive statistics

Standard deviation498.2416498
Coefficient of variation (CV)11.11330891
Kurtosis683.5793187
Mean44.83288043
Median Absolute Deviation (MAD)0
Skewness24.04195078
Sum65994
Variance248244.7416
MonotonicityNot monotonic
2025-02-08T04:59:26.148717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 1419
96.4%
400 11
 
0.7%
500 8
 
0.5%
700 5
 
0.3%
450 4
 
0.3%
600 4
 
0.3%
2000 4
 
0.3%
1200 2
 
0.1%
480 2
 
0.1%
2500 2
 
0.1%
Other values (11) 11
 
0.7%
ValueCountFrequency (%)
0 1419
96.4%
54 1
 
0.1%
350 1
 
0.1%
400 11
 
0.7%
450 4
 
0.3%
ValueCountFrequency (%)
15500 1
 
0.1%
8300 1
 
0.1%
3500 1
 
0.1%
2500 2
0.1%
2000 4
0.3%

MoSold
Real number (ℝ)

Distinct12
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.320652174
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-02-08T04:59:26.308959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median6
Q38
95-th percentile11
Maximum12
Range11
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.699881934
Coefficient of variation (CV)0.4271524298
Kurtosis-0.4023985218
Mean6.320652174
Median Absolute Deviation (MAD)2
Skewness0.2117822911
Sum9304
Variance7.289362457
MonotonicityNot monotonic
2025-02-08T04:59:26.493209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6 254
17.3%
7 235
16.0%
5 208
14.1%
4 142
9.6%
8 124
8.4%
3 106
7.2%
10 90
 
6.1%
11 79
 
5.4%
9 64
 
4.3%
12 59
 
4.0%
Other values (2) 111
7.5%
ValueCountFrequency (%)
1 58
 
3.9%
2 53
 
3.6%
3 106
7.2%
4 142
9.6%
5 208
14.1%
ValueCountFrequency (%)
12 59
4.0%
11 79
5.4%
10 90
6.1%
9 64
4.3%
8 124
8.4%

YrSold
Real number (ℝ)

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2007.817935
Minimum2006
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-02-08T04:59:26.669755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2006
5-th percentile2006
Q12007
median2008
Q32009
95-th percentile2010
Maximum2010
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.330295051
Coefficient of variation (CV)0.0006625576095
Kurtosis-1.194474815
Mean2007.817935
Median Absolute Deviation (MAD)1
Skewness0.09505857233
Sum2955508
Variance1.769684923
MonotonicityNot monotonic
2025-02-08T04:59:26.807141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
2009 341
23.2%
2007 331
22.5%
2006 317
21.5%
2008 305
20.7%
2010 178
12.1%
ValueCountFrequency (%)
2006 317
21.5%
2007 331
22.5%
2008 305
20.7%
2009 341
23.2%
2010 178
12.1%
ValueCountFrequency (%)
2010 178
12.1%
2009 341
23.2%
2008 305
20.7%
2007 331
22.5%
2006 317
21.5%

SaleType
Text

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
2025-02-08T04:59:26.940960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length2
Mean length2.157608696
Min length2

Characters and Unicode

Total characters3176
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWD
2nd rowWD
3rd rowWD
4th rowWD
5th rowWD
ValueCountFrequency (%)
wd 1278
86.8%
new 123
 
8.4%
cod 43
 
2.9%
conld 9
 
0.6%
conli 5
 
0.3%
conlw 5
 
0.3%
cwd 4
 
0.3%
oth 3
 
0.2%
con 2
 
0.1%
2025-02-08T04:59:27.247523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
D 1334
42.0%
W 1282
40.4%
w 128
 
4.0%
N 123
 
3.9%
e 123
 
3.9%
C 68
 
2.1%
O 46
 
1.4%
o 21
 
0.7%
n 21
 
0.7%
L 19
 
0.6%
Other values (3) 11
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2877
90.6%
Lowercase Letter 299
 
9.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 1334
46.4%
W 1282
44.6%
N 123
 
4.3%
C 68
 
2.4%
O 46
 
1.6%
L 19
 
0.7%
I 5
 
0.2%
Lowercase Letter
ValueCountFrequency (%)
w 128
42.8%
e 123
41.1%
o 21
 
7.0%
n 21
 
7.0%
t 3
 
1.0%
h 3
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3176
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 1334
42.0%
W 1282
40.4%
w 128
 
4.0%
N 123
 
3.9%
e 123
 
3.9%
C 68
 
2.1%
O 46
 
1.4%
o 21
 
0.7%
n 21
 
0.7%
L 19
 
0.6%
Other values (3) 11
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 1334
42.0%
W 1282
40.4%
w 128
 
4.0%
N 123
 
3.9%
e 123
 
3.9%
C 68
 
2.1%
O 46
 
1.4%
o 21
 
0.7%
n 21
 
0.7%
L 19
 
0.6%
Other values (3) 11
 
0.3%

SaleCondition
Text

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
2025-02-08T04:59:27.468646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.158288043
Min length6

Characters and Unicode

Total characters9065
Distinct characters18
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNormal
2nd rowNormal
3rd rowNormal
4th rowAbnorml
5th rowNormal
ValueCountFrequency (%)
normal 1207
82.0%
partial 126
 
8.6%
abnorml 103
 
7.0%
family 20
 
1.4%
alloca 12
 
0.8%
adjland 4
 
0.3%
2025-02-08T04:59:27.772252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1495
16.5%
l 1480
16.3%
r 1436
15.8%
m 1330
14.7%
o 1322
14.6%
N 1207
13.3%
i 146
 
1.6%
P 126
 
1.4%
t 126
 
1.4%
A 119
 
1.3%
Other values (8) 278
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7589
83.7%
Uppercase Letter 1476
 
16.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1495
19.7%
l 1480
19.5%
r 1436
18.9%
m 1330
17.5%
o 1322
17.4%
i 146
 
1.9%
t 126
 
1.7%
n 107
 
1.4%
b 103
 
1.4%
y 20
 
0.3%
Other values (3) 24
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
N 1207
81.8%
P 126
 
8.5%
A 119
 
8.1%
F 20
 
1.4%
L 4
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 9065
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1495
16.5%
l 1480
16.3%
r 1436
15.8%
m 1330
14.7%
o 1322
14.6%
N 1207
13.3%
i 146
 
1.6%
P 126
 
1.4%
t 126
 
1.4%
A 119
 
1.3%
Other values (8) 278
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9065
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1495
16.5%
l 1480
16.3%
r 1436
15.8%
m 1330
14.7%
o 1322
14.6%
N 1207
13.3%
i 146
 
1.6%
P 126
 
1.4%
t 126
 
1.4%
A 119
 
1.3%
Other values (8) 278
 
3.1%

SalePrice
Real number (ℝ)

Distinct663
Distinct (%)45.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180793.248
Minimum34900
Maximum755000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-02-08T04:59:27.939905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34900
5-th percentile88000
Q1129975
median163000
Q3214000
95-th percentile325793.2
Maximum755000
Range720100
Interquartile range (IQR)84025

Descriptive statistics

Standard deviation79320.98214
Coefficient of variation (CV)0.4387386312
Kurtosis6.534696908
Mean180793.248
Median Absolute Deviation (MAD)38000
Skewness1.880511671
Sum266127661
Variance6291818207
MonotonicityNot monotonic
2025-02-08T04:59:28.122742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
140000 20
 
1.4%
135000 17
 
1.2%
145000 15
 
1.0%
155000 14
 
1.0%
110000 13
 
0.9%
190000 13
 
0.9%
115000 12
 
0.8%
160000 12
 
0.8%
139000 11
 
0.7%
130000 11
 
0.7%
Other values (653) 1334
90.6%
ValueCountFrequency (%)
34900 1
0.1%
35311 1
0.1%
37900 1
0.1%
39300 1
0.1%
40000 1
0.1%
ValueCountFrequency (%)
755000 1
0.1%
745000 1
0.1%
625000 1
0.1%
611657 1
0.1%
582933 1
0.1%